U.S. patent application number 15/048858 was filed with the patent office on 2016-06-16 for predictive model development system applied to enterprise risk management.
This patent application is currently assigned to Asset Reliance, Inc.. The applicant listed for this patent is Asset Reliance, Inc.. Invention is credited to Jeffrey Scott Eder.
Application Number | 20160171398 15/048858 |
Document ID | / |
Family ID | 40253902 |
Filed Date | 2016-06-16 |
United States Patent
Application |
20160171398 |
Kind Code |
A1 |
Eder; Jeffrey Scott |
June 16, 2016 |
Predictive Model Development System Applied To Enterprise Risk
Management
Abstract
An automated method, computer readable storage device and system
for using artificial intelligence based cognitive learning methods
to develop predictive models and then use said models to measure
and manage risk for an organization on a continual basis. The
elements of value, external factors and segments of value of the
organization are analyzed and modeled using predictive models and
causal models that are developed by learning from the data
associated with said organization. Scenarios of both normal and
extreme situations are also developed by learning from the data.
The scenarios are then used to drive simulations of the predictive
models. The output from these simulations are then used to
calculate and display a matrix of risk.
Inventors: |
Eder; Jeffrey Scott; (Mill
Creek, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Asset Reliance, Inc. |
Bothell |
WA |
US |
|
|
Assignee: |
Asset Reliance, Inc.
Bothell
WA
|
Family ID: |
40253902 |
Appl. No.: |
15/048858 |
Filed: |
February 19, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10748890 |
Dec 30, 2003 |
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15048858 |
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Current U.S.
Class: |
705/7.28 |
Current CPC
Class: |
G06Q 40/06 20130101;
G06Q 10/06375 20130101; G06Q 10/067 20130101; G06Q 10/0635
20130101; G06Q 10/06 20130101; G06Q 30/0201 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A system comprising: one or more computers; and one or more data
storage devices having instructions stored thereon that, when
executed by the computers, cause the computers to perform
operations comprising: prepare a plurality of data representative
of an organization that physically exists for processing where said
organization comprises a plurality of segments of value, where one
or more elements of value and one or more external factors has a
net contribution or impact on a value of each of the segments of
value and where each of the elements of value and each of the
external factors consists of a plurality of items, develop a linear
or a nonlinear predictive model for each of the plurality of
segments of value that quantifies the impact by item of the
elements of value and the external factors on the value the segment
of value by item by learning from at least part of said integrated
data, identify one or more scenarios by learning from the
integrated data, simulate an organization financial performance
using said predictive models under each scenario in order to
quantify a plurality of organization risks by item for each segment
of value, and output a matrix of risk for the organization
containing the risks by segment of value and item.
2. The system of claim 1, wherein developing a linear or a
nonlinear predictive model for each of the segments of value that
quantifies an impact by item of the elements of value and the
external factors on a value of the segments of value by learning
from at least part of said integrated data comprises: using a
plurality of predictive models and a plurality of causal models to
analyze and select a portion of the integrated data to use as an
input when modeling an impact of each of the one or more elements
of value; using the plurality of predictive models and the
plurality of causal models to analyze and select a portion of the
integrated data to use as an input when modeling an impact each of
the one or more external factors; learning which algorithm from a
plurality of linear and nonlinear predictive model algorithms to
include in the model for each of the segments of value in order to
model a net contribution or impact of each of the one or more
elements of value by item and each of each of the one or more
external factors by item to a value of each of the segments of
value; learning which model from a plurality of causal models
comprises a best fit for modeling the contribution of the elements
of value and the external factors to the value of each of the
segments of value when using the selected data; learning if a
clustering of the input data improves an accuracy of the segment of
value models; learning a relative contribution of each of the
elements of value to the value of each of the segments of value,
learning a relative contribution of each of the external factors to
the value of each of the segments of value, and learning a relative
contribution of each of the external factors to the organization
value where the plurality of causal models are selected from the
group consisting of Tetrad, LaGrange, Bayesian and path analysis
and where the plurality of predictive models are selected from the
group consisting of classification and regression tree; projection
pursuit regression; generalized additive model (GAM), redundant
regression network; neural network, multivariate adaptive
regression splines; linear regression; and stepwise regression.
3. The system of claim 1, wherein the method further comprises
identifying one or more changes at the item level that will jointly
optimize two or more aspects of an organization financial
performance selected from the group consisting of a total
organization return, a total organization risk and a total
organization value.
4. The system of claim 1, wherein the one or more scenarios are
selected from the group consisting of normal and extreme where the
extreme scenario is developed by using a peak over threshold
algorithm.
5. The system of claim 1, wherein the one or more elements of value
physically exist, are selected from the group consisting of:
alliances, brands, channels, customers, employees, information
technology, intellectual property, processes and vendors and are
each represented in the linear or a nonlinear predictive model for
each of the segments of value by a vector comprised of causal
variables associated with said element of value where said vectors
are generated by an algorithm selected from the group consisting of
LaGrange, Bayesian and path analysis.
6. The system of claim 1, wherein the plurality of segments of
value are selected from the group consisting of current operation,
derivatives, investments, real options and market sentiment where
developing a model of the market sentiment segment of value
comprises a top down analysis of organization value and risk and
where developing a model of the current operation segment of value
comprises a bottom up analysis of organization value and risk.
7. The system of claim 1, wherein the plurality of risks are
selected from the group consisting of event risks, element
variability, factor variability and volatility where each risk
consists of an expected reduction in value and where an event risk
with a known expected reduction in value comprises a contingent
liability that is measured using a real option algorithm.
8. A non-transitory computer-readable storage device encoded with a
computer program product, the computer program product comprising
instructions that when executed by one or more computers cause the
one or more computers to perform operation comprising: prepare a
plurality of data representative of an organization that physically
exists for processing where said organization comprises a plurality
of segments of value, where one or more elements of value and one
or more external factors has a net contribution or impact on a
value of each of the segments of value and where each of the
elements of value and each of the external factors consists of a
plurality of items, develop a linear or a nonlinear predictive
model for each of the segments of value that quantifies the impact
by item of the elements of value and the external factors on the
value the segment of value by item by learning from at least part
of said integrated data, identify one or more scenarios by learning
from the integrated data, simulate an organization financial
performance using said predictive models under each scenario in
order to quantify a plurality of organization risks by item for
each segment of value, and output a matrix of risk for the
organization containing the risks by segment of value and item.
9. The computer readable storage device of claim 8, wherein
developing a linear or a nonlinear predictive model for each of the
segments of value that quantifies an impact by item of the elements
of value and the external factors on a value of the segments of
value by learning from at least part of said integrated data
comprises: using a plurality of predictive models and a plurality
of causal models to analyze and select a portion of the integrated
data to use as an input when modeling an impact of each of the one
or more elements of value; using the plurality of predictive models
and the plurality of causal models to analyze and select a portion
of the integrated data to use as an input when modeling an impact
each of the one or more external factors; learning which algorithm
from a plurality of linear and nonlinear predictive model
algorithms to include in the model for each of the segments of
value in order to model a net contribution or impact of each of the
one or more elements of value by item and each of each of the one
or more external factors by item to a value of each of the segments
of value; learning which model from a plurality of causal models
comprises a best fit for modeling the contribution of the elements
of value and the external factors to the value of each of the
segments of value when using the selected data; learning if a
clustering of the input data improves an accuracy of the segment of
value models; learning a relative contribution of each of the
elements of value to the value of each of the segments of value,
learning a relative contribution of each of the external factors to
the value of each of the segments of value, and learning a relative
contribution of each of the external factors to the organization
value where the plurality of causal models are selected from the
group consisting of Tetrad, LaGrange, Bayesian and path analysis
and where the plurality of predictive models are selected from the
group consisting of classification and regression tree; projection
pursuit regression; generalized additive model (GAM), redundant
regression network; neural network, multivariate adaptive
regression splines; linear regression; and stepwise regression.
10. The computer readable storage device of claim 8, wherein the
method further comprises identifying one or more changes at the
item level that will jointly optimize two or more aspects of an
organization financial performance selected from the group
consisting of a total organization return, a total organization
risk and a total organization value.
11. The computer readable storage device of claim 8, wherein the
one or more scenarios are selected from the group consisting of
normal and extreme where the extreme scenario is developed by using
a peak over threshold algorithm.
12. The computer readable storage device of claim 8, wherein the
one or more elements of value physically exist and are each
represented in the linear or a nonlinear predictive model for each
of the segments of value by a vector comprised of causal variables
associated with said element of value.
13. The computer readable storage device of claim 8, wherein the
plurality of segments of value are selected from the group
consisting of current operation, derivatives, investments, real
options, and market sentiment where developing a model of the
market sentiment segment of value comprises a top down analysis of
organization value and risk and where developing a model of the
current operation segment of value comprises a transaction driven,
bottom up analysis of organization value and risk.
14. The computer readable storage device of claim 8, wherein the
plurality of risks are selected from the group consisting of event
risks, element variability, factor variability and volatility where
each risk consists of an expected reduction in value and where an
event risk with a known expected reduction in value comprises a
contingent liability that is measured using a real option
algorithm.
15. A system comprising: one or more computers; and one or more
data storage devices having instructions stored thereon that, when
executed by the computers, cause the computers to perform
operations comprising: training each of a plurality of different
types of predictive models using training data, wherein the
predictive models include a plurality of each type of predictive
model that are trained with different combinations of features of
the training data; generating, for each of the plurality of trained
predictive models, a measure that represents an estimation of an
effectiveness of the respective trained predictive models;
selecting two or more of the plurality of trained predictive models
based on the respective measures of the trained predictive models;
obtaining a respective predictive output from each of the selected
predictive models in the two or more trained predictive models
using the input data; combining the predictive outputs to generate
a result.
16. The system of claim 15, wherein training each of the plurality
of different types of predictive models using the training data
comprises: using a plurality of different types of predictive
models to analyze and select a portion of the training data to use
as an input to the predictive models; learning if a clustering of
the selected portion of the training data improves an accuracy of
any of the predictive models; learning which model from a plurality
of causal models comprises a best fit model when using the selected
portion of the training data and then refining the selected portion
of the training data to include only the data selected by the best
fit causal model; and learning which algorithm from a plurality of
linear and nonlinear predictive model algorithms comprises a best
fit model when using the refined selection of the training data as
an input; where the plurality of causal models are selected from
the group consisting of Tetrad, LaGrange, Bayesian and path
analysis and where the plurality of different types of predictive
models are selected from the group consisting of classification and
regression tree; projection pursuit regression; generalized
additive model (GAM), redundant regression network; neural network,
multivariate adaptive regression splines; linear regression; and
stepwise regression.
17. The system of claim 15, wherein the measure that represents the
estimation of the effectiveness of the respective trained
predictive models comprises a mean squared error measure.
18. The system of claim 15, wherein learning which model from the
plurality of causal models comprises the best fit model when using
the selected portion of the training data comprises using a cross
validation algorithm to identify the best fit model.
19. The system of claim 15, wherein combining the predictive model
outputs to generate the result further comprises averaging the
predictive model outputs to generate the result.
20. The system of claim 15, wherein learning if the clustering of
the selected portion of the training data improves the accuracy of
any of the predictive models comprises comparing an error measure
for an overall model with a combined error measure from models of
two or more clusters.
Description
CONTINUATION AND CROSS REFERENCE TO RELATED PATENTS
[0001] This application is a continuation U.S. patent application
Ser. No. 10/748,890 filed Dec. 30, 2003 the disclosure of which is
incorporated herein by reference. This application is related to
U.S. patent application Ser. No. 11/360,087 filed Feb. 23, 2006,
the disclosure of which is incorporated herein by reference. U.S.
patent application Ser. No. 11/360,087 is a continuation in part of
U.S. patent application Ser. No. 09/688,983 filed Oct. 17, 2000 the
disclosure of which is incorporated herein by reference. This
application is related to U.S. patent application Ser. No.
10/287,586 filed Nov. 5, 2002, and the disclosure from said
application with respect to vector generation is incorporated
herein by reference. The subject matter of this application is also
related to the subject matter of U.S. Pat. No. 5,615,109 for
"Method of and System for Generating Feasible, Profit Maximizing
Requisition Sets", U.S. patent application Ser. No. 13/548,104
filed Jul. 12, 2012 and U.S. patent application Ser. No. 13/557,836
filed Jul. 20, 2012 the disclosures of which are also incorporated
herein by reference.
BACKGROUND OF THE INVENTION
[0002] This invention relates to a method of and system for
flexibly integrating organization related data, information,
knowledge and systems into a market value matrix and using said
matrix to support the optimization one or more aspects of
organization risk, return and value.
SUMMARY OF THE INVENTION
[0003] It is a general object of the present invention to provide a
novel and useful system for flexibly integrating the data,
information, narrow systems and knowledge bases associated with a
multi-enterprise organization into an overall system for measuring,
managing and optimizing financial performance. A partial list of
the different types of narrow systems is shown in Table 1
below.
TABLE-US-00001 TABLE 1 1. Alliance management systems 2. Asset
management systems for capital and IT assets 3. Brand management
systems 4. Business intelligence systems 5. Call management systems
6. Channel management systems 7. Content management systems 8.
Contract management systems 9. Customer relationship management
systems 10. Demand chain systems 11. Email management systems 12.
Employee relationship management systems 13. Energy risk management
systems 14. Fraud management systems 15. Incentive management
systems 16. Innovation management systems 17. Intellectual property
management systems 18. Investor relationship management systems 19.
Knowledge management systems 20. Location management systems 21.
Maintenance management systems 22. Partner relationship management
systems 23. Performance management systems (for IT assets) 24.
Price optimization systems 25. Private exchanges 26. Product
life-cycle management systems 27. Project portfolio management
systems 28. Risk simulation systems 29. Sales force automation
systems 30. Scorecard systems 31. Service management systems 32.
Six-sigma quality management systems 33. Supplier relationship
management systems 34. Support chain systems 35. Technology chain
systems 36. Unstructured data management systems 37. Visitor (web
site) relationship management systems 38. Weather risk management
systems 39. Workforce management systems 40. Yield management
systems
[0004] The systems in Table 1 come on top of new versions of the
traditional systems that many companies have had in place for some
time including those shown in Table 2 below.
TABLE-US-00002 TABLE 2 1. Basic financial system like a general
ledger* 2. Budgetingginancial planning system 3. Cash management
system 4. Commodity risk management systems 5. Credit-risk
management system 6. Human resource management system* 7. Interest
rate risk management system 8. Material requirement planning
system* 9. Process management systems 10. Project management
systems 11. Risk management information system 12. Strategic
planning system 13. Supply chain system *all 3 applications are
usually bundled within an enterprise resource planning system
Collectively the systems in tables 1 and 2 will be referred to as
the "narrow systems".
[0005] A preferable object to which the present invention is
applied is flexibly integrating the systems, data, information and
knowledge used for measuring, managing and optimizing the assets,
processes, projects and risks associated with the operation of a
multi-enterprise commercial organization.
[0006] Information systems work best when they are aligned with the
goals of the corporation they serve. Given that the goal of
virtually every modern corporation is to improve its financial
performance and maximize shareholder value, a system that provides
a framework for measuring and optimizing financial performance is
also an ideal framework for integrating data, information,
knowledge and systems. This specific framework can also be used to
integrate the information and knowledge from different parts of the
organization to formulate budgets and complete long term plans.
[0007] In a more general sense, establishing a model that serves as
a platform for flexibly integrating data, information, systems and
knowledge from external partners and others in the organization via
a knowledge layer is a new and novel way for coordinating,
controlling and improving the productivity of knowledge workers.
For example, a model for brand development could be established and
then information, systems and knowledge that support brand
development could be flexibly integrated by using the brand
development model as the framework for development of an xml schema
that directs information, systems and knowledge to the appropriate
location within the framework. The same process can be used for any
cell or cell subcategory within the market value matrix.
[0008] An important general feature of the matrix is that its
performance improves steadily as more narrow systems are
integrated. As systems are added, system flexibility is
demonstrated by the fact that there is no specific order in which
narrow systems need to be integrated. Another aspect of system
flexibility is that the narrow systems do not have to be completely
integrated in order to improve the performance of the system. If
narrow system operators choose to limit the integration to
providing access to data from their system, then the system of the
present invention can still function effectively.
[0009] Integrating narrow systems and knowledge bases to the
framework defined by a market value matrix starts by establishing a
standard ontology for account numbers, element of value
descriptions, enterprise names, external factor descriptions, risk
descriptions and units of measure for the transaction data and
descriptive data stored within each of these systems. The
organization standard will be used for all data being processed
within the system of the present invention so all data extracted
for use in the system is first converted to the organization
standard (if necessary) before being stored in the application
database.
[0010] After the organization standard for accounts, elements,
factors, risks and units of measure is established, the next stage
in system integration is to define the segments of value and
elements of value that define the market value matrix. Commercial
businesses can create value in five distinct ways: [0011] 1.
selling products or services that generate positive cash flow;
[0012] 2. developing real options for generating positive cash flow
in the future; [0013] 3. holding investments that produce income
and/or capital gains; [0014] 4. holding derivatives (broadly
defined) that produce income and/or capital gains; and [0015] 5.
generating positive market sentiment. These five methods for
creating value define the segments of value. When they are added
together, the value of these five segments equals the market value
of the enterprise or organization.
[0016] Separating the segments of value is important for a variety
of reasons. Because each segment of value represents a different
way to create value, the methods for valuing each segment are
different. The risks associated with each of the segments of value
are also very different. For example, financial assets like money
in the bank and bonds are far more stable than derivatives that are
highly leveraged. Derivatives can change in value by many orders of
magnitude in an instant. Having said that, it is worth noting that
many types of risk can have an impact on every segment of value.
For example, catastrophic event risk, like the risk of a large
hurricane or terrorist attack, can have an impact on all segments
of value. In a similar fashion external factor variability risk and
strategic risk, can impact all segments of value. The impact of
element variability risk generally has less impact on investments
and derivatives than the preceding two types of risk. The final
type of risk, market volatility is defined as the difference
between the overall market risk of equity for the firm (i.e.
volatility implied by equity option prices) and the calculated
total of the other types of risk.
[0017] Because of the critical importance of the different segments
of value. The first step in defining the framework for enterprise
system integration is therefore defining the segments of value for
the enterprise. The list of the segments of value used in the
system of the present invention are shown below in Table 4.
TABLE-US-00003 TABLE 4 Segment Number Segment Name 10. Current
Operation 11. Revenue 12. Expense 13. Change in Capital 20. Real
Options 21. Real Option Forecast Revenue 22. Real Option Expense
23. Real Option Change in Capital 24. Forecast Contingent Liability
Loss 25. Contingent Liability Expense 26. Contingent Liability
Change in Capital 30. Investments 40. Derivatives 41. Options 42.
Swaps 43. Swaptions 44. Collars 50. Market Sentiment
Other segment names and numbers can be used to the same effect.
Additional subcategories may also be added as desired.
[0018] The five segments of value define one axis of the market
value matrix. The basic outline of the market value matrix will be
completed after specifying the elements of value that define the
other axis of the matrix. The list of standard elements of value
used in the system of the present invention is shown in table 5. It
is worth noting here that the external factors and risks that can
not be assigned to an element of value are included in the "Going
Concern Value" element as shown in table 5.
TABLE-US-00004 TABLE 5 Element Number Element Name 1. Segment Total
10. Financial Assets 11. Cash 12. Short Term Assets/Liabilities 13.
Long Term Assets/Liabilities 20. Tangible Assets 21. Property 22.
Plant 23. Systems 24. Equipment 25. Land 26. Infrastructure 30.
Intangible Assets 31. Brands 32. Channel Partners 33. Customers 34.
Employees 35. Intellectual Property 36. Investors 37. Partners 38.
Processes 39. Suppliers 40. Going Concern Value 41. External
Factors 42. Event Risks 43. Strategic Risks 44. Market Risks
[0019] The segment of value information is used to determine what
type of valuation and/or risk analysis method needs to be used
while the element of value designation groups the data for
analysis. Using the matrix that has just been defined, the cell or
cells in the market value matrix (see FIG. 10) that each of the
narrow systems is "managing" can now be specified by designating a
segment and an element. For example, the position of a supply chain
system would be defined as shown below: [0020] Segment of Value:
Expense (12), Element of Value: Supplier (39) If the organization
also had a supplier relationship management system, then the data
from that system would probably be pointed to the same cell.
Projects, processes and risks generally impact more than one
element of value so the specifications for systems used to manage
these subsets of enterprise operations would be expected to include
a designation for more than one element of value. Locating each
system and knowledge base within the market value matrix is just
the first step in integrating all enterprise systems and knowledge
within the novel system for financial performance measurement,
management and optimization.
[0021] The second step in defining the integration framework is
refining the placement of information within each cell to
distinguish between information related to value and the different
types of risk. This categorization is facilitated by adding
subcategories to each cell within the market value matrix. A cell
is defined by the intersection of the segments and elements of
value. The subcategories are shown in Table 6.
TABLE-US-00005 TABLE 6 Element subcategories 1. Base Value 2.
Element Variability Risk 3. Event Risk 4. Factor Variability Risk
5. Strategic Risk 6. Market Volatility Risk
Using the new subcategories, the position of a supply chain system
could be defined more precisely as shown below: [0022] Segment of
Value: Expense (12), Element of Value: Supplier (39a, 39b) This
designation would be chosen as the supply chain system has
information about the performance of the suppliers. This
performance data would be expected to include both standard
performance information as well as data regarding variability in
performance that may have caused financial distress to the
organization. The processing that separates the two subcategories
(a and b) from the information provided by the supply chain system
will be described later in the detailed specification.
[0023] Mapping each system and knowledge base to a cell within the
market value matrix is a major step in integrating all organization
related data, information, knowledge and systems into the novel
system for financial performance measurement, management and
optimization. The next major step involves identifying what types
of data are being received from the integrated systems. There are
two types of data that are received from each system: performance
data and feature data.
[0024] Feature data are described first. Features encapsulate all
the different options the asset, option, process, project and risk
managers have for managing the portion of the organization they are
responsible for. For example, factor variability risk associated
with fluctuating electricity prices could be minimized by: [0025]
1. installing new equipment that requires less electrical power;
[0026] 2. reducing exposure to electricity prices by entering into
long term supply contracts; and/or [0027] 3. reducing exposure to
electricity prices by purchasing derivatives that "lock-in" price
protection for future purchases. These derivatives could include
options, swaps, swaptions or collars. The best choice may be some
combination of these 3 different "features". Feature options (also
referred to as options) are options to use a feature in the future.
For example, the risk owner could purchase land to install a
co-generation plant--giving the enterprise the real option to
produce its own electricity at some future date. This real option
to produce electricity at a future date could limit the time period
which electricity factor variability damaged the enterprise and it
would be considered a feature option. As detailed later, the system
of the present invention will integrate the enterprise data,
information, knowledge and systems in order to select the set of
features and feature options that maximize the returns and minimize
the risk associated with managing the multi-enterprise
organization.
[0028] For obvious reasons, the fields containing feature data need
to be clearly distinguished from the fields containing transaction
data and descriptive data. Because the system of the present
invention can also be used to develop budgets and long term plans
for the organization, provision is also made for transmitting data
of this type. Within the overall feature data classification the
separate subcategories of information for each feature as shown in
Table 7.
TABLE-US-00006 TABLE 7 Feature data subcategories 1. Current value
(can be yes or no) at system date 2. Maximum value 3. Minimum value
4. time frame to implement 5. cost to implement (capital and
expense) 6. Local optimization value and date 7. Enterprise
optimization value and date 8--Budget Data 9--Long Term Plan Data
10--Remove element
In general the narrow systems and knowledge bases will be providing
the system of the present invention with the current value, the
range of values (maximum value and minimum value), the time period
for implementation and the cost to implement for each feature. The
system of the present invention will complete its processing and
return the feature set that will optimize the financial performance
of the entire enterprise (not just a narrow subset).
[0029] Having detailed the method for managing the integration of
feature data, the next step is to detail the method for integrating
performance data. Performance data includes transaction data and
descriptive data. Because many of the systems being integrated have
their own analytical capabilities, performance data will also
include information derived from transaction data, information
derived from descriptive data and information derived from
transaction and descriptive data. The derived data would be
expected to include: clustered data, statistics regarding the data
(trends, standard deviation, covariance, etc.) and performance
indicators. The usefulness of the derived data are limited for the
same reason the output from these systems is limited--lack of
information regarding interaction with other elements and options,
failure to consider important classes of risk, inability to
consider impact on all segments of value and the absence of a true
enterprise perspective. In spite of these limitations, the derived
data can in some cases be used in system processing. The use of
this derived data eliminates the need for the system of the present
invention to repeat the same calculations. Use of the derived data
requires an understanding of the type of processing that has been
completed. As with feature data, performance data includes budget
and long term plan data. This information is communicated using the
categories shown in Table 8.
TABLE-US-00007 TABLE 8 Processing Level by Element 1. Raw Data 2.
Clustered Data 3. Cluster Criteria 4. Value Driver Candidate (aka
performance indicator) 5. Composite Variable 6. Value Driver 7.
Independent, Causal Value Driver 8. Combination Factor or Element
9. Vector 10--segment #. Value for segment # 11--segment #. Element
risk for segment # 12--segment #. Factor risk for segment #
13--segment #. Event risk for segment # 14--segment #. Strategic
risk for segment # 15--segment #. Base market risk for segment #
16--segment #Market volatility risk for segment #. 17--Budget Data
18--Long Term Plan Data Statistics by Element aa. Mean ab. Time
Period for Mean ac. Standard Deviation ad. Time Period for Standard
Deviation ae. Rolling Quarterly Average af. Time Period for Rolling
Quarterly Average ag. Market Covariance ah. Time Period for Market
Covariance ai. Slope aj. Time Period for Slope ak. Event risk
probability al. Event risk cost
The categories listed in Table 8 can be expanded or contracted in
order to cover all types of risk the company is subject to as well
as all the processing completed by the narrow systems.
[0030] In addition to using the standard described above for
identifying the knowledge bases and the information obtained from
narrow systems, this same standard is used when processing data and
storing the results of system processing. As a result, information
can be accessed at any point by anyone in order to determine the
financial status of the multi-company organization and/or the
companies within the organization. We will refer to data that has
the integration identification information attached to it as
"tagged data". Clearly tagging all processed data will facilitate
the automated delivery of new products and services from financial
service providers and other partners.
[0031] Implementing the integration method with existing
applications can take any of several forms including:
pre-programmed templates with specified tag assignments for each
narrow system and knowledge base, the use of wizards to guide data
tag assignments, extensions to existing xml based standards, the
specification of the data tags by knowledge base and narrow system
operators in the data they make available for transfer or some
combination of the first four options. In one embodiment, the
knowledge base and narrow system operators will include the
specified tags in the data they make available for transfer and
they will identify the matrix cell or cells that their data
pertains to in the information made available to others. In one
embodiment this information will be integrated with the system of
the present invention via a knowledge layer in an operating system
and the information and knowledge will be made available to all
enterprise systems and to partner systems via the same layer.
[0032] While one embodiment of the novel system for integrating
narrow systems and knowledge analyzes element and external factor
impacts on all five segments of value, the system can operate when
one or more of the segments of value are missing for one or more
enterprises and/or for the organization as a whole. For example,
the organization may be a value chain that does not have a market
value in which case there will be no market sentiment to evaluate.
Another common situation would be a multi-company corporation that
has no derivatives in most of the enterprises (or companies) within
the overall structure. The system is also capable of analyzing a
single enterprise. As detailed later, the segments of value that
are present in each enterprise are defined in the system settings
table (140). Virtually all public companies will have at least
three segments of value: current operation, real options and market
sentiment. However, it is worth noting only one segment of value is
required per enterprise for operation of the system. Because most
corporations have only one traded stock, multi-enterprise (aka
multi-company) corporations will generally define an enterprise for
the "corporate shell" to account for all market sentiment. This
"corporate shell" enterprise can also be used to account for any
joint options the different companies within the corporation may
collectively possess. The system is also capable of analyzing the
value of the organization without considering all types of risk.
However, the system needs to complete the value analysis before it
can complete the analysis of all organization risks.
[0033] The innovative system has the added benefit of providing a
large amount of detailed information to the organization users
concerning both tangible and intangible elements of value by
enterprise. Because intangible elements are by definition not
tangible, they can not be measured directly. They must instead be
measured by the impact they have on their surrounding environment.
There are analogies in the physical world. For example, electricity
is an "intangible" that is measured by the impact it has on the
surrounding environment. Specifically, the strength of the magnetic
field generated by the flow of electricity through a conductor
turns a shaft in a motor and the torque of the shaft is used to
determine the amount of electricity that is being consumed. The
system of the present invention measures tangible and intangible
elements of value by identifying the attributes that, like the
magnetic field, reflect the strength of the element in driving
segments of value (current operation, investments, real options,
derivatives, market sentiment) and/or components of value (revenue,
expense and change in capital) within the current operation and are
relatively easy to measure. Once the attributes related to the
strength of each element are identified, they can be summarized
into a single expression (a vector) if the attributes don't
interact with attributes from other elements. If attributes from
one element drive those from another, then the elements can be
combined for analysis and/or the impact of the individual
attributes can be summed together to calculate a value for the
element. In one embodiment, vectors are used to summarize the
impact of the element attributes. The vectors for all elements are
then evaluated to determine their relative contribution to driving
each of the components of value and/or each of the segments of
value. The system of the present invention calculates the product
of the relative contribution and the forecast longevity of each
element to determine the relative contribution to each of the
elements of value to each segment of value. The contribution of
each element to each enterprise is then determined by summing the
element contribution to each segment of value. The value
contribution of external factors is determined using the same
process described for element evaluation. The organization value is
then calculated by summing the value of all the enterprises within
the organization
[0034] In accordance with the invention, the automated extraction
of data from existing narrow systems and knowledge bases
significantly increases the scale and scope of the analysis that
can be completed. The system of the present invention further
enhances the efficiency and effectiveness of the analysis by
automating the retrieval, storage and analysis of information
useful for analyzing elements of value, segments of value and
organization risks from external databases, external publications
and the Internet. To facilitate its use as a tool for financial
management, the system of the present invention produces intuitive
graphical reports and reports in formats that are similar to the
reports provided by traditional accounting systems. Integrating
information from all enterprise systems is just one way the system
of the present invention overcomes the limitations of existing
methods and systems.
[0035] The method for integrating the numerous, narrow business
management systems provided by the present invention eliminates the
need for custom interface development. It also eliminates the need
to use six different standards in operating an enterprise wide
financial management system. Most importantly the system of the
present invention completely integrates all of the narrowly focused
enterprise systems and knowledge bases into an overall system for
measuring, managing and optimizing organizational financial
performance. The level of integration enabled by the system of the
present invention will also support: the creation of new financial
products; the creation of new financial services; the automated
delivery of new financial products and services; the automated
delivery of traditional financial products and services; and the
integration of narrow systems with other applications.
[0036] By providing real-time financial insight to users of every
system in the organization, the integrated system of the present
invention enables the continuous optimization of management
decision making across an entire multi-enterprise organization.
BRIEF DESCRIPTION OF DRAWINGS
[0037] These and other objects, features and advantages of the
present invention will be more readily apparent from the following
description of one embodiment of the invention in which:
[0038] FIG. 1 is a block diagram showing the major processing steps
of the present invention;
[0039] FIG. 2 is a diagram showing the files or tables in the
application database (50) of the present invention that are
utilized for data storage and retrieval during the processing in
the innovative system for multi-enterprise organization analysis
and optimization;
[0040] FIG. 3 is a block diagram of an implementation of the
present invention;
[0041] FIG. 4 is a block diagram showing the sequence of steps in
the present invention used for specifying system settings and for
integrating with other systems;
[0042] FIG. 5A, FIG. 5B and FIG. 5C are block diagrams showing the
sequence of steps in the present invention used for preparing data
obtained from the narrow systems for processing by the system of
the present invention;
[0043] FIG. 6A, FIG. 6B, FIG. 6C and FIG. 6D are block diagrams
showing the sequence of steps in the present invention used for
creating, analyzing and optimizing the market value matrix for the
organization by enterprise;
[0044] FIG. 7 is a block diagram showing the sequence in steps in
the present invention used in defining and displaying reports and
completing special analyses;
[0045] FIG. 8 is a diagram showing the data windows that are used
for receiving information from and transmitting information to the
user (20) during system processing;
[0046] FIG. 9 is a diagram showing how the enterprise matrices of
risk can be combined to calculate the organizational matrix of
risk; and
[0047] FIG. 10 is a diagram showing how the enterprise market value
matrices can be combined to calculate the market value matrix for
the organization;
[0048] FIG. 11 is a sample report showing the efficient frontier
for Organization XYZ and the current position of XYZ relative to
the efficient frontier and the market frontier; and
[0049] FIG. 12 is a sample report showing the efficient frontier
for Organization XYZ, the current position of XYZ relative to the
efficient frontier and the forecast of the new position of XYZ
relative to the efficient frontier after user specified changes are
implemented.
DETAILED DESCRIPTION OF ONE EMBODIMENT
[0050] FIG. 1 provides an overview of the processing completed by
the innovative system for measuring, managing and continuously
optimizing the market value matrix for a multi-enterprise
organization. In accordance with the present invention, an
automated method of and system (100) for identifying the features
in the optimal market value matrix for a multi-enterprise
commercial organization is provided. Processing starts in this
system (100) with the specification of system settings and the
flexible integration (200) of the system of the present invention
with a basic financial system (5), an operation management system
(10), a web site management system (12), a human resource
information system (15), a risk management system (17), an external
database (25), an advanced financial system (30), an asset
management system (35), a supply chain system (37), a knowledge
base (36) and a partner system (39) via a network (45). The system
integration progress may be influenced by a user (20) through
interaction with a user-interface portion of the application
software (700) that mediates the display, transmission and receipt
of all information to and from browser software (800) such as the
Netscape Navigator or the Microsoft Internet Explorer in an access
device (90) such as a phone, pda or personal computer where data
are entered by the user (20).
[0051] While only one system and database of each type (5, 10, 12,
15, 17, 25, 30, 35, 36, 37 and 39) is shown in FIG. 1, it is to be
understood that the system (100) can integrate with all narrow
systems listed in Tables 1 and 2 and multiple knowledge bases. In
one embodiment at least one system of each type listed (5, 10, 12,
15, 17, 25, 30, 35, 36, 37 and 39) will be integrated with the
system (100) via the network (45) for each enterprise within the
organization. While the data from multiple asset management systems
can be utilized in the analysis of each element of value completed
by the system of the present invention, one embodiment of the
present invention contains only one asset management system for
each element of value being analyzed for each enterprise within the
organization. Integrating all the asset management systems ensures
that every asset--tangible or intangible--is considered within the
overall financial framework for the organization. It should also be
understood that it is possible to complete a bulk extraction of
data from each database (5, 10, 12, 15, 17, 25, 30, 35, 36, 37 and
39) and the Internet (40) via the network (45) using peer to peer
networking and data extraction applications before initializing the
data bots. The data extracted in bulk could be stored in a single
datamart, a data warehouse or a storage area network where the data
bots could operate on the aggregated data or the data could be left
in the original databases and extracted as needed for calculations
by the bots over a network (45).
[0052] All extracted information is stored in a file or table
(hereinafter, table) within an application database (50) as shown
in FIG. 2. The application database (50) contains tables for
storing user input, extracted information and system calculations
including a system settings table (140), a cash flow table (141), a
real option value table (142), a matrix data table (143), a data
request table (144), a semantic map table (145), a frame definition
table (146), a benchmark return table (147), an analysis definition
table (148), a bot date table (149), a financial forecasts table
(150), a classified text table (151), a scenarios table (152), a
vector table (153), an industry ranking table (154), a report table
(155), an summary data table (156), a simulation table (157) and a
feature rank table (158).
[0053] The application database (50) can optionally exist as a
datamart, data warehouse, a virtual repository or storage area
network. The system of the present invention has the ability to
accept and store supplemental or primary data directly from user
input, a data warehouse or other electronic files in addition to
receiving data from the databases described previously. The system
of the present invention also has the ability to complete the
necessary calculations without receiving data from one or more of
the specified databases. However, in one embodiment all required
information is obtained from the specified data sources (5, 10, 12,
15, 17, 25, 30, 35, 36, 37, 39 and 40) for each enterprise in the
organization.
[0054] As shown in FIG. 3, one embodiment of the present invention
is a computer system (100) illustratively comprised of a
user-interface personal computer (110) connected to an
application-server personal computer (120) via a network (45). The
application-server personal computer (120) is in turn connected via
the network (45) to a database-server personal computer (130). The
user interface personal computer (110) is also connected via the
network (45) to an Internet browser appliance (90) that contains
browser software (800) such as Microsoft Internet Explorer or
Netscape Navigator.
[0055] The database-server personal computer (130) has a read/write
random access memory (131), a hard drive (132) for storage of the
application database (50), a keyboard (133), a communication bus
(134), a display (135), a mouse (136), a CPU (137) and a printer
(138).
[0056] The application-server personal computer (120) has a
read/write random access memory (121), a hard drive (122) for
storage of the non-user-interface portion of the enterprise section
of the application software (200, 300, 400 and 500) of the present
invention, a keyboard (123), a communication bus (124), a display
(125), a mouse (126), a CPU (127) and a printer (128). While only
one client personal computer is shown in FIG. 3, it is to be
understood that the application-server personal computer (120) can
be networked to fifty or more client, user-interface personal
computers (110) via the network (45). The application-server
personal computer (120) can also be networked to fifty or more
server, personal computers (130) via the network (45). It is to be
understood that the diagram of FIG. 3 is merely illustrative of one
embodiment of the present invention.
[0057] The user-interface personal computer (110) has a read/write
random access memory (111), a hard drive (112) for storage of a
client data-base (49) and the user-interface portion of the
application software (700), a keyboard (113), a communication bus
(114), a display (115), a mouse (116), a CPU (117) and a printer
(118).
[0058] The application software (200, 300, 400, and 500) controls
the performance of the central processing unit (127) as it
completes the calculations required to support the production of
the matrices of value and risk for a commercial enterprise. In the
embodiment illustrated herein, the application software program
(200, 300, 400 and 500) is written in a combination of C++, Java
and Visual Basic.RTM.. The application software (200, 300, 400 and
500) can use Structured Query Language (SQL) for extracting data
from the databases and the Internet (5, 10, 12, 15, 17, 25, 30, 35,
36, 37, 39 and 40). The user (20) can optionally interact with the
user-interface portion of the application software (700) using the
browser software (800) in the browser appliance (90) to provide
information to the application software (200, 300, 400 and 500) for
use in determining which data will be extracted and transferred to
the application database (50) by the data bots.
[0059] User input is initially saved to the client database (49)
before being transmitted to the communication bus (124) and on to
the hard drive (122) of the application-server computer via the
network (45). Following the program instructions of the application
software, the central processing unit (127) accesses the extracted
data and user input by retrieving it from the hard drive (122)
using the random access memory (121) as computation workspace in a
manner that is well known.
[0060] The computers (110, 120, 130) shown in FIG. 3 illustratively
are personal computers or workstations that are widely available.
Typical memory configurations for client personal computers (110)
used with the present invention should include at least 512
megabytes of semiconductor random access memory (111) and at least
a 100 gigabyte hard drive (112). Typical memory configurations for
the application-server personal computer (120) used with the
present invention should include at least 2056 megabytes of
semiconductor random access memory (121) and at least a 250
gigabyte hard drive (122). Typical memory configurations for the
database-server personal computer (130) used with the present
invention should include at least 4112 megabytes of semiconductor
random access memory (131) and at least a 500 gigabyte hard drive
(132).
[0061] Using the system described above, the market value matrix is
used as a template to guide the integration of the narrowly focused
enterprise systems into a system for measuring and optimizing the
financial performance of a multi-enterprise organization. The
market value matrix is also used as a template to structure the
knowledge stored in the organization by enterprise.
[0062] In one embodiment, the revenue, expense and capital
requirement forecasts for the current operation, the real options
and the contingent liabilities are obtained from an advanced
financial planning system database (30) derived from an advanced
financial planning system similar to the one disclosed in U.S. Pat.
No. 5,615,109. The extracted revenue, expense and capital
requirement forecasts are used to calculate a cash flow for each
period covered by the forecast for each enterprise by subtracting
the expense and change in capital for each period from the revenue
for each period. A steady state forecast for future periods is
calculated after determining the steady state growth rate that best
fits the calculated cash flow for the forecast time period. The
steady state growth rate is used to calculate an extended cash flow
forecast. The extended cash flow forecast is used to determine the
Competitive Advantage Period (CAP) implicit in the enterprise
market value.
[0063] Before going further, we need to define a number of terms
that will be used throughout the detailed description of one
embodiment:
1) A transaction is any event that is logged or recorded; 2)
Transaction data are any data related to a transaction; 3)
Descriptive data are any data related to any item, segment of
value, element of value, component of value, risk or external
factor that is logged or recorded that is not transaction data.
Descriptive data includes forecast data and other data calculated
by the system of the present invention; 4) An element of value (or
element) is "an entity or group that as a result of past
transactions, forecasts or other data has provided and/or is
expected to provide economic benefit to one or more segments of
value of the enterprise"; 5) An item is a single member of the
group that defines an element of value. For example, an individual
salesman would be an "item" in the "element of value" sales staff.
It is possible to have only one item in an element of value; 6)
Item variables are the transaction data and descriptive data
associated with an item or related group of items; 7) Item
performance indicators are data derived from transaction data
and/or descriptive data; 8) Composite variables for an element are
mathematical or logical combinations of item variables and/or item
performance indicators; 9) Element variables or element data are
the item variables, item performance indicators and composite
variables for a specific element or sub-element of value; 10)
External factors (or factors) are numerical indicators of:
conditions or prices external to the enterprise and conditions or
performance of the enterprise compared to external expectations of
conditions or performance; 11) Factor variables are the transaction
data and descriptive data associated with external factors; 12)
Factor performance indicators are data derived from factor
transaction data and/or descriptive data; 13) Composite factors are
mathematical or logical combinations of factor variables and/or
factor performance indicators for a factor; 14) Factor data are the
factor variables, factor performance indicators and composite
factors for external factors; 15) A layer is software and/or
information that gives an application or layer the ability to
interact with another layer, application or set of information at a
general or abstract level rather than at a detailed level, web
services are the functional equivalents of layers in a web services
environment; 16) An operating system is a program that manages:
hardware, other programs, web services, and/or the interaction
between any combination of hardware, other programs and web
services. For example, a computer operating system manages the
interaction between other programs in a computer. In a similar
fashion, a network operating system manages the interaction between
hardware and applications on a network. The programs and/or
hardware make use of the operating system by making requests for
services through defined procedures. In addition, users can
interact directly with the operating system through a user
interface such as a command language or a graphical user interface;
17) An enterprise is a commercial enterprise with one revenue
component of value (note: it is possible to define a commercial
enterprise that has more than one revenue component of value); 18)
A value chain is defined to be enterprises that have joined
together to deliver a product and/or a service to a customer; 19) A
multi-company corporation is a corporation that participates in
more than one distinct line of business. The distinctiveness of a
given line of business is determined by the elements of value that
support the business. If more than 50% of the elements of value
that support a revenue stream are unique to that revenue stream,
then that revenue stream defines a "distinct" line of business; 20)
Multi enterprise organizations include value chains and
multi-company corporations. Partnerships between government
agencies and private companies and/or between two government
agencies are also defined as multi-enterprise organizations; 21)
Frames are sub-sets of an enterprise, sub-sets of a
multi-enterprise organization, enterprise combination or
organization combination that can be analyzed separately. For
example, one frame could group together all the elements, external
factors and other risks from the market value matrix package by
process allowing different processes to be analyzed by outside
vendors. Another frame could exclude the market sentiment segment
of value from each enterprise within a multi-enterprise
organization. Frames can also be used to collect budget information
and long term plan information; 22) Risk is defined as events or
variability that may cause losses and/or diminished financial
performance for an enterprise or organization; 23) Variability risk
is risk of financial damage caused by variability. Variability can
be caused by: external factors (i.e. commodity prices, interest
rates, exchange rates, ideas, market level, etc.) and elements of
value within an enterprise (i.e. processes, equipment, employees,
etc.). There is also variability risk associated with the market
price of equity for the organization. Variability risk is generally
quantified using statistical measures like standard deviation per
month, per year or over some other time period. The covariance
between different variability risks is also determined as
simulations require quantified information regarding the
inter-relationship between the different risks to perform
effectively; 24) Factor variability (or factor variability risk) is
the risk of damage caused by external factor variability; 25)
Element variability (or element variability risk) is the risk of
damage caused by variability of elements of value; 26) Market
variability is defined as the implied variability associated with
enterprise or organization equity. The implied amount of this
variability can be determined by analyzing the option prices for
company equity. 27) Event risk is the risk of financial damage
caused by an event. Most insurance policies cover event risks. For
example, an insurance policy might state in essence that: if this
event happens, then we will reimburse event related expenses up to
a pre-determined amount. Event risks that are covered by insurance
are typically associated with damage to people and property that
are caused by accidents, the weather (hurricanes, tornadoes) and
acts of nature (earthquakes, volcanoes, etc.). Other events that
can cause damage like customer defection, employee resignation,
etc. are generally not covered by insurance and as a result many
companies overlook their impact. Event risks are generally tracked
using modified database programs that keep track of each occurrence
of each type of risk, its cause, cost and the amount of money that
was reimbursed. These programs can be used to analyze historical
patterns and develop forecasts. The forecasts are often used in
forecasting the expected frequency of different events, the cost
associated with each event and the associated dollar value of the
risk that should be insured; 29) Standard event risks will be
defined as those risks that have a one time impact. 30) Strategic
risk (or strategic event risk) is the risk associated with events
that can have a permanent impact on the financial prospects of an
enterprise or organization. Examples of strategic risk would
include: the risk that a large new competitor enters the market,
the risk of a catastrophe so large that the company is wiped out
and the risk that a new technology renders existing products
obsolete; 31) Base market risk is defined as the implied
variability associated with a portfolio that represents the market.
For example, the S&P 500 can be used in the U.S. and the FTSE
100 can be used in the U.K. The implied amount of this variability
can be determined by analyzing the option prices for company
equity; 32) Industry market risk is defined as the implied
variability associated with a portfolio that is in the same SIC
code as the enterprise or organization-industry market risk can be
substituted for base market risk in order to get a clearer picture
of the market risk specific to the organization (or enterprise)
stock; 33) Market volatility (or market risk sentiment), is the
different between market variability risk and the calculated values
of: base market risk, factor variability, element variability,
event risk and strategic event risk over a given time period; 34)
Narrow systems are the systems listed in Tables 1 and 2 and any
other system that supports the analysis, measurement or management
of an element, segment, factor, process or risk of an organization
or enterprise; 35) Real options are defined as options the
organization may have to make a change in its operation at some
future date--these can include the introduction of a new product,
the ability to shift production to lower cost environments, etc.
Real options are generally supported by the elements of value of an
organization; 36) Contingent liabilities are liabilities the
organization may have at some future date, the liability is
contingent on some event occurring in the future, therefore they
can be considered as a type of event risk. However, because they
are valued using real option algorithms, they are included in the
real option segment of value; and 37) The efficient frontier is
defined as the maximum return the organization can expect for a
given level of risk. It is similar in concept to the "efficient
frontier" from portfolio management theory however it is different
in several respects. For example, the efficient frontier for
portfolios only identifies the investments that should be in or out
of the portfolio to provide the maximum return for a given level of
risk. The efficient frontier in the system of the present invention
is defined by the feature set for all elements of value and risk
that provides the highest expected return. In general the mix of
assets, options and risks changes little--the frontier is reached
by operating the assets, options and risks more effectively. The
efficient frontier for portfolios is determined by making tradeoffs
versus a single measure of risk while the efficient frontier
defined by the novel system of the present invention makes
tradeoffs relative to six different types of risk and other
features that are all inter-related.
[0064] We will use the terms defined above when detailing one
embodiment of the present invention. In this invention, analysis
bots are used to determine element of value lives and the
percentage of each segment of value that is attributable to each
element of value (and external factor) by enterprise. The resulting
values are then added together to determine the valuation for each
element (and external factor). This process is illustrated by the
example in Table 9 for the current operation segment of value
(which is divided into 3 components of value--revenue, expense and
capital change for more detailed analysis). External factor values
are calculated in a similar manner, however, they generally do not
have defined lives.
TABLE-US-00008 TABLE 9 Element Gross Value Percentage Life/CAP* Net
Value Revenue value = $120 M 20% 80% Value = $19.2 M Expense value
= ($80 M) 10% 80% Value = ($6.4) M Capital value = ($5 M) 5% 80%
Value = ($0.2) M Total value = $35 M Net value for this element:
Value = $12.6 M *CAP = Competitive Advantage Period
[0065] The integration and optimization of the different knowledge
bases and systems for the multi-enterprise organization is
completed in four distinct stages. As shown in FIG. 4, (block 200
from FIG. 1) the first stage of processing integrates the system of
the present invention with the other systems within each enterprise
of the multi-enterprise organization. This integration facilitates
the extraction of required data and the return of optimized feature
sets to the integrated systems for implementation. As shown in FIG.
5A, FIG. 5B and FIG. 5C, the second stage of processing (block 300
from FIG. 1) prepares data from the narrow systems for the analysis
of business value and risk by enterprise. As shown in FIG. 6A, FIG.
6B, FIG. 6C and FIG. 6D the third stage of processing (block 400
from FIG. 1) continually defines the market value matrix that
quantifies the impact of the elements of value and risks on the
segments of value by enterprise (see FIG. 10), and defines the
efficient frontier for organization financial performance. As shown
in FIG. 7, the fourth stage of processing (block 500 from FIG. 1)
displays the market value matrix and the efficient frontier for the
organization and analyzes the impact of changes in structure and/or
operation on the financial performance of the multi-enterprise
organization. If the operation is continuous, then the processing
described above is continuously repeated.
System Integration
[0066] The flow diagram in FIG. 4 details the processing that is
completed by the portion of the application software (200) that
integrates with other applications in order to support knowledge
integration and organization optimization. As discussed previously,
the system of the present invention is capable of integrating the
narrowly focused systems listed in Tables 1 and 2. Operation of the
system (100) is illustrated by describing the integration of the
system (100) with the basic financial system, the operation
management system, the web site management system, the human
resource system, the risk management system, an external database,
an advanced financial system, an asset management system, a
knowledge base and a supply chain system. Communications are
completed between the system of the present invention and the:
basic financial system database (5), operation management system
database (10), web site management system database (12), human
resource information system database (15), risk management system
database (17), external database (25), advanced financial system
database (30), asset management system database (35), knowledge
base (36), supply chain system database (37), partner system (39)
and the Internet (40) by enterprise. A brief overview of the
different systems will be presented before reviewing each step of
processing completed by this portion (200) of the application
software.
[0067] Corporate financial software systems are generally divided
into two categories: basic and advanced. Advanced financial systems
utilize information from the basic financial systems to perform
financial analysis, financial forecasting, financial planning and
financial reporting functions. Virtually every commercial
enterprise uses some type of basic financial system as they are
generally required to use these systems to maintain books and
records for income tax purposes. An increasingly large percentage
of these basic financial systems are resident in computer systems
and intranets. Basic financial systems include general-ledger
accounting systems with associated accounts receivable, accounts
payable, capital asset, inventory, invoicing, payroll and
purchasing subsystems. These systems incorporate worksheets, files,
tables and databases. These databases, tables and files contain
information about the enterprise operations and its related
accounting transactions. As will be detailed below, these
databases, tables and files are accessed by the application
software of the present invention in order to extract the
information required for enterprise measurement, management and
optimization. The system is also capable of extracting the required
information from a data warehouse (or datamart) when the required
information has been loaded into the warehouse.
[0068] General ledger accounting systems generally store only valid
accounting transactions. As is well known, valid accounting
transactions consist of a debit component and a credit component
where the absolute value of the debit component is equal to the
absolute value of the credit component. The debits and the credits
are posted to the separate accounts maintained within the
accounting system. Every basic accounting system has several
different types of accounts. The effect that the posted debits and
credits have on the different accounts depends on the account type
as shown in Table 10.
TABLE-US-00009 TABLE 10 Account Type: Debit Impact: Credit Impact:
Asset Increase Decrease Revenue Decrease Increase Expense Increase
Decrease Liability Decrease Increase Equity Decrease Increase
General ledger accounting systems also require that the asset
account balances equal the sum of the liability account balances
and equity account balances at all times.
[0069] The general ledger system generally maintains summary,
dollar only transaction histories and balances for all accounts
while the associated subsystems, accounts payable, accounts
receivable, inventory, invoicing, payroll and purchasing, maintain
more detailed historical transaction data and balances for their
respective accounts. It is common practice for each subsystem to
maintain the detailed information shown in Table 11 for each
transaction.
TABLE-US-00010 TABLE 11 Subsystem Detailed Information Accounts
Vendor, Item(s), Transaction Date, Amount Owed, Due Payable Date,
Account Number Accounts Customer, Transaction Date, Product Sold,
Quantity, Price, Receivable Amount Due, Terms, Due Date, Account
Number Capital Asset ID, Asset Type, Date of Purchase, Purchase
Price, Assets Useful Life, Depreciation Schedule, Salvage Value
Inventory Item Number, Transaction Date, Transaction Type,
Transaction Qty, Location, Account Number Invoicing Customer Name,
Transaction Date, Product(s) Sold, Amount Due, Due Date, Account
Number Payroll Employee Name, Employee Title, Pay Frequency, Pay
Rate, Account Number Purchasing Vendor, Item(s), Purchase Quantity,
Purchase Price(s), Due Date, Account Number
[0070] As is well known, the output from a general ledger system
includes income statements, balance sheets and cash flow statements
in well defined formats which assist management in measuring the
financial performance of the firm during the prior periods when
data input and system processing have been completed.
[0071] While basic financial systems are similar between firms,
operation management systems vary widely depending on the type of
company they are supporting. These systems typically have the
ability to not only track historical transactions but to forecast
future performance. For manufacturing firms, operation management
systems such as Enterprise Resource Planning Systems (ERP),
Material Requirement Planning Systems (MRP), Purchasing Systems,
Scheduling Systems and Quality Control Systems are used to monitor,
coordinate, track and plan the transformation of materials and
labor into products. Systems similar to the one described above may
also be useful for distributors to use in monitoring the flow of
products from a manufacturer.
[0072] Operation Management Systems in manufacturing firms may also
monitor information relating to the production rates and the
performance of individual production workers, production lines,
work centers, production teams and pieces of production equipment
including the information shown in Table 12.
TABLE-US-00011 TABLE 12 Operation Management System--Production
Information 1. ID number (employee id/machine id) 2. Actual
hours--last batch 3. Standard hours--last batch 4. Actual
hours--year to date 5. Actual/Standard hours--year to date % 6.
Actual setup time--last batch 7. Standard setup time--last batch 8.
Actual setup hours--year to date 9. Actual/Standard setup hrs--yr
to date % 10. Cumulative training time 11. Job(s) certifications
12. Actual scrap--last batch 13. Scrap allowance--last batch 14.
Actual scrap/allowance--year to date 15. Rework time/unit last
batch 16. Rework time/unit year to date 17. QC rejection
rate--batch 18. QC rejection rate--year to date
[0073] Operation management systems are also useful for tracking
requests for service to repair equipment in the field or in a
centralized repair facility. Such systems generally store
information similar to that shown below in Table 13.
TABLE-US-00012 TABLE 13 Operation Management System--Service Call
Information 1. Customer name 2. Customer number 3. Contract number
4. Service call number 5. Time call received 6. Product(s) being
fixed 7. Serial number of equipment 8. Name of person placing call
9. Name of person accepting call 10. Promised response time 11.
Promised type of response 12. Time person dispatched to call 13.
Name of person handling call 14. Time of arrival on site 15. Time
of repair completion 16. Actual response type 17. Part(s) replaced
18. Part(s) repaired 19. 2nd call required 20. 2nd call number
[0074] Web site management system databases keep a detailed record
of every visit to a web site, they can be used to trace the path of
each visitor to the web site and upon further analysis can be used
to identify patterns that are most likely to result in purchases
and those that are most likely to result in abandonment. This
information can also be used to identify which promotion would
generate the most value for the enterprise using the system. Web
site management systems generally contain the information shown in
Table 14.
TABLE-US-00013 TABLE 14 Web site management system database 1.
Customer's URL 2. Date and time of visit 3. Pages visited 4. Length
of page visit (time) 5. Type of browser used 6. Referring site 7.
URL of site visited next 8. Downloaded file volume and type 9.
Cookies 10. Transactions
Computer based human resource systems may some times be packaged or
bundled within enterprise resource planning systems such as those
available from SAP, Oracle and Peoplesoft. Human resource systems
are increasingly used for storing and maintaining corporate records
concerning active employees in sales, operations and the other
functional specialties that exist within a modern corporation.
Storing records in a centralized system facilitates timely,
accurate reporting of overall manpower statistics to the corporate
management groups and the various government agencies that require
periodic updates. In some cases, human resource systems include the
enterprise payroll system as a subsystem. In one embodiment of the
present invention, the payroll system is part of the basic
financial system. These systems can also be used for detailed
planning regarding future manpower requirements. Human resource
systems typically incorporate worksheets, files, tables and
databases that contain information about the current and past
employees. As will be detailed below, these databases, tables and
files are accessed by the application software of the present
invention in order to extract the information required for
completing a business valuation. It is common practice for human
resource systems to store the information shown in Table 15 for
each employee.
TABLE-US-00014 TABLE 15 Human Resource System Information 1.
Employee name 2. Job title 3. Job code 4. Rating 5. Division 6.
Department 7. Employee No./(Social Security Number) 8. Year to
date--hours paid 9. Year to date--hours worked 10. Employee start
date--enterprise 11. Employee start date--department 12. Employee
start date--current job 13. Training courses completed 14.
Cumulative training expenditures 15. Salary history 16. Current
salary 17. Educational background 18. Current supervisor
[0075] Risk management system databases (17) contain statistical
data about the past behavior and forecasts of likely future
behavior of interest rates, currency exchange rates, weather,
commodity prices and key customers (credit risk systems). They also
contain detailed information about the composition and mix of risk
reduction products (derivatives, insurance, etc.) the enterprise
has purchased. Some companies also use risk management systems to
evaluate the desirability of extending or increasing credit lines
to customers. The information from these systems is used to
supplement the risk information developed by the system of the
present invention.
[0076] External databases can be used for obtaining information
that enables the definition and evaluation of a variety of things
including elements of value, external factors, industry real
options and event risks. In some cases, information from these
databases can be used to supplement information obtained from the
other databases and the Internet (5, 10, 12, 15, 17, 30, 35, 36,
37, 39 and 40). In the system of the present invention, the
information extracted from external databases (25) includes the
data listed in Table 16.
TABLE-US-00015 TABLE 16 Types of information 1) Numeric information
such as that found in the SEC Edgar database and the databases of
financial infomediaries such as FirstCall, IBES and Compustat, 2)
Text information such as that found in the Lexis Nexis database and
databases containing past issues from specific publications, 3)
Risk management products such as derivatives, swaps and
standardized insurance contracts that can be purchased on line, 4)
Geospatial data; 5) Multimedia information such as video and audio
clips 6) Event risk data including information about the likelihood
of a loss and the magnitude of such a loss
[0077] The system of the present invention uses different "bot"
types to process each distinct data type from external databases
(25). The same "bot types" are also used for extracting each of the
different types of data from the Internet (40). The system of the
present invention must have access to at least one data source
(usually, an external database (25)) that provides information
regarding the equity prices for each enterprise and the equity
prices and financial performance of the competitors for each
enterprise.
[0078] Advanced financial systems may also use information from
external databases (25) and the Internet (40) in completing their
processing. Advanced financial systems include financial planning
systems and activity based costing systems. Activity based costing
systems may be used to supplement or displace the operation of the
expense component analysis segment of the present invention.
Financial planning systems generally use the same format used by
basic financial systems in forecasting income statements, balance
sheets and cash flow statements for future periods. Management uses
the output from financial planning systems to highlight future
financial difficulties with a lead time sufficient to permit
effective corrective action and to identify problems in enterprise
operations that may be reducing the profitability of the business
below desired levels. These systems are most often developed by
individuals within companies using two and three-dimensional
spreadsheets such as Lotus 1-2-3 .RTM., Microsoft Excel.RTM. and
Quattro Pro.RTM.. In some cases, financial planning systems are
built within an executive information system (EIS) or decision
support system (DSS). For one embodiment of the present invention,
the advanced finance system database is similar to the financial
planning system database detailed in U.S. Pat. No. 5,165,109 for
"Method of and System for Generating Feasible, Profit Maximizing
Requisition Sets", by Jeff S. Eder.
[0079] While advanced financial planning systems have been around
for some time, asset management systems are a relatively recent
development. Their appearance is further proof of the increasing
importance of "soft" assets. Asset management systems include:
customer relationship management systems, partner relationship
management systems, channel management systems, knowledge
management systems, visitor relationship management systems,
intellectual property management systems, investor management
systems, vendor management systems, alliance management systems,
process management systems, brand management systems, workforce
management systems, human resource management systems, email
management systems, IT management systems and/or quality management
systems. Asset management systems are similar to operation
management systems in that they generally have the ability to
forecast future events as well as track historical occurrences. As
discussed previously, many of these systems have added analytical
capabilities that allow them to identify trends and patterns in the
data associated with the asset they are managing. Customer
relationship management systems are the most well established asset
management systems at this time and will be the focus of the
discussion regarding asset management system data. In firms that
sell customized products, the customer relationship management
system is generally integrated with an estimating system that
tracks the flow of estimates into quotations, orders and eventually
bills of lading and invoices. In other firms that sell more
standardized products, customer relationship management systems
generally are used to track the sales process from lead generation
to lead qualification to sales call to proposal to acceptance (or
rejection) and delivery. All customer relationship management
systems would be expected to track all of the customer's
interactions with the enterprise after the first sale and store
information similar to that shown below in Table 17.
TABLE-US-00016 TABLE 17 Customer Relationship Management
System--Information 1. Customer/Potential customer name 2. Customer
number 3. Address 4. Phone number 5. Source of lead 6. Date of
first purchase 7. Date of last purchase 8. Last sales call/contact
9. Sales call history 10. Sales contact history 11. Sales history:
product/qty/price 12. Quotations: product/qty/price 13. Custom
product percentage 14. Payment history 15. Current A/R balance 16.
Average days to pay
[0080] Supply chain systems could be considered as asset management
systems as they are used to manage a critical asset--supplier
relationships. However, because of their importance and visibility
they are listed separately. Supply chain system databases (37)
contain information that may have been in operation management
system databases (10) in the past. These systems provide enhanced
visibility into the availability of goods and promote improved
coordination between customers and their suppliers. All supply
chain systems would be expected to track all of the items ordered
by the enterprise after the first purchase and store information
similar to that shown below in Table 18.
TABLE-US-00017 TABLE 18 Supply chain system Information 1. Stock
Keeping Unit (SKU) 2. Vendor 3. Total quantity on order 4. Total
quantity in transit 5. Total quantity on back order 6. Total
quantity in inventory 7. Quantity available today 8. Quantity
available next 7 days 9. Quantity available next 30 days
10.Quantity available next 90 days 11. Quoted lead time 12. Actual
average lead time
Project management systems, process management systems and risk
management systems can also be integrated with the system of the
present invention by mapping their data to the market value matrix
in a manner similar to that described for systems focused on the
management of one element of value. These systems would in general
have data that relates to more than one matrix cell.
[0081] System processing of the information from the different
databases (5, 10, 12, 15, 17, 25, 30, 35, 36, 37, 39) and the
Internet (40) described above starts in a block 201, FIG. 4. The
software in block 201 prompts the user (20) via the system settings
data window (701) to provide system setting information. The system
setting information entered by the user (20) is transmitted via the
network (45) back to the application-server (120) where it is
stored in the system settings table (140) in the application
database (50) in a manner that is well known. The specific inputs
the user (20) is asked to provide at this point in processing are
shown in Table 19.
TABLE-US-00018 TABLE 19 1. New calculation or structure revision?
2. Continuous, If yes, new calculation frequency? (by minute, hour,
day, week) 3. Organization structure (enterprises) 4. Enterprise
structures (segments of value, elements of value, external factors
etc.) 5. Enterprise industry classifications (SIC Code) 6. Names of
primary competitors by SIC Code 7. Keywords (brands, etc.) 8.
Baseline account structure 9. Baseline units of measure 10. Base
currency 11. Geocoding standard 12. The maximum number of
generations to be processed without improving fitness 13. Default
clustering algorithm (selected from list) and maximum cluster
number 14. Number of months a product is considered new after it is
first produced 15. Default management report types (text, graphic,
both) 16. Default missing data procedure 17. Maximum time to wait
for user input 18. Risk free interest rate 19. Maximum number of
sub elements 20. Confidence interval for risk reduction programs
21. Simulation (aka risk and return analysis) time periods 22.
Dates for history (optional) 23. Minimum working capital level
(optional) 24. Detailed valuation using components of current
operation value? (yes or no) 25. Use of industry real options? (yes
or no) 26. Semantic mapping? (yes or no) 27. Industry portfolio
(optional) 28. Market portfolio (for base market risk calculation)
29. Most likely scenario--mix of normal and extreme (default is
normal)
[0082] The system settings data are used by the software in block
201 to develop a market value matrix for each enterprise in the
organization. The market value matrix is defined by the segments of
value, elements of value and external factors. The subcategories
for each element of value include the element base value, element
variability risk, external factor variability risk, event risk,
strategic event risk and market risk. The application of the
remaining system settings will be further explained as part of the
detailed explanation of the system operation. The software in block
201 also uses the current system date to determine the time periods
(generally in months) that require data to complete the
calculations. In one embodiment the analysis of value and risk by
the system utilizes data from every data source for the four year
period before and the three year forecast period after the
specified valuation date and/or the date of system calculation. The
user (20) also has the option of specifying the data periods that
will be used for completing system calculations. After the date
range is calculated it is stored in the system settings table
(140), processing advances to a software block 210.
[0083] The software in block 210 establishes one or more operating
system layers in order to communicate via a network (45) with the
different databases (5, 10, 12, 15, 17, 25, 30, 35, 36, 37, 39)
that are being integrated within the novel system for integration.
While any number of methods can be used to identify the different
data sources, in one embodiment the systems are identified using
UDDI protocols and the systems include information that identifies
the cell or cells within the market value matrix that their stored
information pertains to as described previously. The data within
each database that is available for extraction is tagged as
described previously. The software in block 210 operates
continuously to extract and store data in the market value matrix
in accordance with the xml schema described previously. Processing
in the system of the present invention continues in a software
block 303 that prepares the extracted data for analysis.
[0084] After the system processing described below has been
completed, the tagged set of optimized features for each narrow
system and the entire market value matrix are sent by a software
block 511 back to a software block 210. The software in block 210
uses one or more operating system layers to make information
continually available to the narrow systems, supplier systems and
to partner systems that can provide the necessary security
information to access one or more of the layers. The information
that is available to narrow systems, partner systems and supplier
systems via a network (45) includes: [0085] 1. Packets containing
optimized sets of feature data and customized context data. The
optimized feature data will bring the organization closer to the
efficient frontier when implemented. The context data in the
packets are customized in accordance with the location of the
narrow system within the market value matrix. More specifically,
the narrow systems are provided with information concerning the
portions of the market value matrix that are impacted by the
portion of the market value matrix they are analyzing/managing. The
statistical information developed in later stages of processing
detailed below and stored in the matrix data table (143) is used
for quantifying the inter-relationships in order to determine what
information needs to included in each customized data packet. In
addition to information about inter-relationships related to value
and risk creation, operational data such as inventory position,
order status, etc. is also included (note this could be included in
a separate packet or accessed separately from a central location).
In this way, each narrow system can make an accurate estimate
regarding the likely impact on the enterprise and organization of
changes in their features; and [0086] 2. Packets containing
knowledge from the knowledge bases that have been integrated with
the market value matrix structure are also made available. This can
include technical knowledge, procedure knowledge and physical
characteristics about the organization and its elements, factors,
processes, projects and risks.
[0087] The software in block 210 also stores requests for
information from partner systems such as those disclosed in
cross-referenced application Ser. No. 10/012,374, filed Dec. 12,
2001 in the data request table (144) and transmits data
transmissions to the financial service providers that have been
approved by the user (20).
Data Preparation
[0088] The flow diagrams in FIG. 5A, FIG. 5B and FIG. 5C, detail
the processing that is completed by the portion of the application
software (300) that prepares data for analysis.
[0089] The software in block 303 immediately passes processing to a
software block 305. The software in block 305 checks the system
settings table (140) and the matrix data table (143) to see if data
are missing from any of the periods required for system
calculation. The software in block 201 previously calculated and
stored the range of required dates. If there are no data missing
from any required period--other than derivative values which will
be evaluated later--then processing advances to a software block
310. Alternatively, if there are missing data for any field except
derivative values for any period, then processing advances to a
block 306.
[0090] The software in block 306 prompts the user (20) via the
missing data window (704) to specify the method to be used for
filling the blanks for each field that is missing data. Options the
user (20) can choose for filling the blanks include: the average
value for the item over the entire time period, the average value
for the item over a specified period, zero, the average of the
preceding item and the following item values and direct user input
for each missing item. If the user (20) does not provide input
within a specified interval, then the default missing data
procedure specified in the system settings table (140) is used.
When all the blanks have been filled and stored for all of the
missing data, system processing advances to a block 310.
[0091] The software in block 310 prompts the user (20) via the
frame definition window (705) to specify frames for analysis.
Frames are sub-sets of each enterprise that can be analyzed at the
value driver level separately. For example, the user (20) may wish
to examine value and/or risk by country, by division, by project,
by process, by action, by program or by manager. Frames can also be
used for special purposes like collecting budget data. The software
in block 310 saves the frame definitions the user (20) specifies in
the frame definition table (146) by enterprise in the application
database (50) before processing advances to a software block
311.
[0092] The software in block 311 assigns one or more frame
designations to all element data and factor data that were stored
in the matrix data table (143) in the prior stage (200) of
processing. After storing the revised element and factor data
records in the matrix data table (143), the software in the block
retrieves the element, segment and external factor definitions from
the system settings table (140) and updates and saves the revised
definitions in order to reflect the impact of new frame definitions
before processing advances to a software block 312.
[0093] The software in block 312 checks the matrix data table (143)
to see if there are frame assignments for all element and factor
data. If there are frame assignments for all data, then processing
advances to a software block 321. Alternatively, if there are data
without frame assignments, then processing advances to a software
block 313.
[0094] The software in block 313 retrieves data from the matrix
data table (143) that don't have frame assignments and then prompts
the user (20) via the frame assignment window (707) to specify
frame assignments for these variables. The software in block 313
saves the frame assignments the user (20) specifies as part of the
data record for the variable in the matrix data table (143) by
enterprise before processing advances to software block 321.
[0095] The software in block 321 checks the system settings table
(140) to see if semantic mapping is being used. If semantic mapping
is not being used, then processing advances to a block 324.
Alternatively, if the software in block 321 determines that
semantic mapping is being used, processing advances to a software
block 322.
[0096] The software in block 322 checks the bot date table (149)
and deactivates inference bots with creation dates before the
current system date and retrieves information from the system
settings table (140) and the classified text table (151). The
software in block 322 then initializes inference bots for each
keyword (including competitor name) in the system settings table
(140) and the classified text table (151) to activate with the
frequency specified by user (20) in the system settings table
(140).
[0097] Bots are independent components of the application that have
specific tasks to perform. In the case of inference bots, their
task is to use Bayesian inference algorithms to determine the
characteristics that give meaning to the text associated with
keywords and classified text previously stored in the application
database (50). Every inference bot contains the information shown
in Table 20.
TABLE-US-00019 TABLE 20 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Keyword 8. Classified text mapping information
After being activated, the inference bots determine the
characteristics that give the text meaning in accordance with their
programmed instructions with the frequency specified by the user
(20) in the system settings table (140). The information defining
the characteristics that give the text meaning is stored in the
semantic map table (145) and any new keywords identified during the
processing are stored in the classified text table (151) in the
application database (50) before processing advances to block
324.
[0098] The software in block 324 checks the bot date table (149)
and deactivates text bots with creation dates before the current
system date and retrieves information from the system settings
table (140), the classified text table (151) and the semantic map
table (145). The software in block 324 then initializes text bots
for each keyword stored in the two tables. The bots are programmed
to activate with the frequency specified by user (20) in the system
settings table (140).
[0099] Bots are independent components of the application that have
specific tasks to perform. In the case of text bots, their tasks
are to locate, count, classify and extract keyword matches from the
external database (25) and the asset management system database
(35) (note: this includes unstructured text) and then store the
results as item variables in the specified location. The
classification includes both the enterprise matrix cell (or cells)
that the keyword is associated with and the context of the keyword
mention in accordance with the semantic map that defines context.
This dual classification allows the system of the present invention
to identify both the number of times a keyword was mentioned and
the context in which the keyword appeared. Every bot initialized by
software block 324 will store the extracted location, count, date
and classification data it discovers in the classified text table
(151) by matrix cell, by enterprise. Every text bot contains the
information shown in Table 21.
TABLE-US-00020 TABLE 21 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Storage location 4. Mapping information 5. Organization
6. Enterprise 7. Data source 8. Keyword 9. Storage location 10.
Semantic map
[0100] After being initialized, the bots locate data from the
external database (25) or the asset management system database (35)
in accordance with its programmed instructions with the frequency
specified by user (20) in the system settings table (140). As each
bot locates and extracts text data, processing advances to a
software block 325 before the bot completes data storage. The
software in block 325 checks to see if all keyword hits are
classified by enterprise, matrix cell and semantic map. If the
software in block 325 does not find any unclassified "hits", then
the address, count and classified text are stored in the classified
text table (151) by enterprise. Alternatively, if there are terms
that have not been classified, then processing advances to a block
330. The software in block 330 prompts the user (20) via the
identification and classification rules window (703) to provide
classification rules for each new term. The information regarding
the new classification rules is stored in the semantic map table
(145) while the newly classified text is stored in the classified
text table (151) by enterprise. It is worth noting at this point
that the activation and operation of bots with classified data (50)
continues. Only bots with unclassified fields "wait" for user input
before completing data storage. The new classification rules will
be used the next time bots are initialized in accordance with the
frequency established by the user (20). In either event, system
processing then passes on to software block 326.
[0101] The software in block 326 checks the bot date table (149)
and deactivates internet text and linkage bots with creation dates
before the current system date and retrieves information from the
system settings table (140), the classified text table (151) and
the semantic map table (145). The software in block 326 then
initializes text bots for each keyword stored in the two tables.
The bots are programmed to activate with the frequency specified by
user (20) in the system settings table (140).
[0102] Bots are independent components of the application that have
specific tasks to perform. In the case of internet text and linkage
bots, their tasks are to locate, count, classify and extract
keyword matches and linkages from the Internet (40) and then store
the results as item variables in a specified location. The
classification includes the enterprise matrix cell (or cells) that
the keyword is associated with, the context of the keyword mention
in accordance with the semantic map that defines context and the
links associated with the keyword. Every bot initialized by
software block 326 will store the extracted location, count, date,
classification and linkage data it discovers in the classified text
table (151) by matrix cell, by enterprise. Multimedia data can be
processed using these same bots if software to translate and parse
the multimedia content is included in each bot. Every Internet text
and linkage bot contains the information shown in Table 22.
TABLE-US-00021 TABLE 22 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Storage location 4. Mapping information 5. Home URL 6.
Organization 7. Enterprise 8. Keyword 9. Semantic map
After being initialized, the text and linkage bots locate and
classify data from the Internet (40) in accordance with their
programmed instructions with the frequency specified by user (20)
in the system settings table (140). As each bot locates and
classifies data from the Internet (40) processing advances to a
software block 325 before the bot completes data storage. The
software in block 325 checks to see if all linkages and keyword
hits have been classified by enterprise, matrix cell and semantic
map. If the software in block 325 does not find any unclassified
"hits" or "links", then the address, counts, dates, linkages and
classified text are stored in the classified text table (151) by
enterprise. Alternatively, if there are hits or links that haven't
been classified, then processing advances to a block 330. The
software in block 330 prompts the user (20) via the identification
and classification rules window (703) to provide classification
rules for each new hit or link. The information regarding the new
classification rules is stored in the semantic map table (145)
while the newly classified text and linkages are stored in the
classified text table (151) by enterprise. It is worth noting at
this point that the activation and operation of bots where all
fields map to the application database (50) continues. Only bots
with unclassified fields will "wait" for user input before
completing data storage. The new classification rules will be used
the next time bots are initialized in accordance with the frequency
established by the user (20). In either event, system processing
then passes on to a software block 351.
[0103] The software in block 351 checks the matrix data table (143)
in the application database (50) to see if there are historical
values for all the derivatives stored in the table. Because SFAS
133 is still not fully implemented, some companies may not have
data regarding the value of their derivatives during a time period
where data are required. If there are values stored for all
required time periods, then processing advances to a software block
355. Alternatively, if there are periods when the value of one or
more derivatives has not been stored, then processing advances to a
software block 352. The software in block 352 retrieves the
required data from the matrix data table (143) in order to value
each derivative using a risk neutral valuation method for the time
period or time periods that are missing values. The algorithms used
for this analysis can include Quasi Monte Carlo or equivalent
Martingale. Other algorithms can be used to the same effect. When
the calculations are completed, the resulting values are stored in
the matrix data table (143) by enterprise and processing advances
to software block 355.
[0104] The software in block 355 calculates pre-defined attributes
by item for each numeric item variable in the matrix data table
(143) and the classified text table (151). The attributes
calculated in this step include: summary data like cumulative total
value; ratios like the period to period rate of change in value;
trends like the rolling average value, comparisons to a baseline
value like change from a prior years level and time lagged values
like the time lagged value of each numeric item variable. The
software in block 355 calculates similar attributes for the text
and geospatial item variables stored in the matrix data table
(143). The software in block 355 also calculates attributes for
each item date variable in the matrix data table (143) and the
classified text table (151) including summary data like time since
last occurrence and cumulative time since first occurrence; and
trends like average frequency of occurrence and the rolling average
frequency of occurrence. The numbers derived from the item
variables are collectively referred to as "item performance
indicators". The software in block 355 also calculates
pre-specified combinations of variables called composite variables
for measuring the strength of the different elements of value. The
item performance indicators and the composite variables are tagged
and stored in the matrix data table (143) or the classified text
table (151) by enterprise before processing advances to a block
356.
[0105] The software in block 356 uses attribute derivation
algorithms such as the AQ program to create combinations of the
variables that were not pre-specified for combination. While the AQ
program is used in one embodiment of the present invention, other
attribute derivation algorithms, such as the LINUS algorithms, may
be used to the same effect. The software creates these attributes
using both item variables that were specified as "element"
variables and item variables that were not. The resulting composite
variables are tagged and stored in the matrix data table (143)
before processing advances to a block 357.
[0106] The software in block 357 derives external factor indicators
for each factor numeric data field stored in the matrix data table
(143). For example, external factors include: the ratio of
enterprise earnings to expected earnings, the number and amount of
jury awards, commodity prices, the inflation rate, growth in gross
domestic product, enterprise earnings volatility vs. industry
average volatility, short and long term interest rates, increases
in interest rates, insider trading direction and levels, industry
concentration, consumer confidence and the unemployment rate that
have an impact on the market price of the equity for an enterprise
and/or an industry. The external factor indicators derived in this
step include: summary data like cumulative totals, ratios like the
period to period rate of change, trends like the rolling average
value, comparisons to a baseline value like change from a prior
years price and time lagged data like time lagged earnings
forecasts. In a similar fashion the software in block 357
calculates external factors for each factor date field in the
matrix data table (143) including summary factors like time since
last occurrence and cumulative time since first occurrence; and
trends like average frequency of occurrence and the rolling average
frequency of occurrence. The numbers derived from numeric and date
fields are collectively referred to as "factor performance
indicators". The software in block 357 also calculates
pre-specified combinations of variables called composite factors
for measuring the strength of the different external factors. The
external factors, factor performance indicators and the composite
factors are tagged and stored in the matrix data table (143) by
matrix cell before processing advances to a block 360.
[0107] The software in block 360 uses attribute derivation
algorithms, such as the Linus algorithm, to create combinations of
the external factors that were not pre-specified for combination.
While the Linus algorithm is used in one embodiment of the present
invention, other attribute derivation algorithms, such as the AQ
program, may be used to the same effect. The software creates these
attributes using both external factors that were included in
"composite factors" and external factors that were not. The
resulting composite variables are tagged and stored in the matrix
data table (143) by matrix cell before processing advances to a
block 361.
[0108] The software in block 361 uses pattern-matching algorithms
to classify data fields for elements of value and external factors
to pre-defined groups with numerical values. This type of analysis
is useful in classifying transaction patterns as "heavy", "light",
"moderate" or "sporadic". This analysis can be used to classify web
site activity, purchasing patterns and advertising frequency among
other things. The numeric values associated with the
classifications are item performance indicators. They are tagged
and stored in the matrix data table (143) by matrix cell before
processing advances to a block 362.
[0109] The software in block 362 retrieves data from the system
settings table (140) and the matrix data table (143) in order to
calculate the historical risk and return for the market portfolio
identified by the user (20) in the system settings table. After the
calculation is completed, the resulting value is saved in the
benchmark return table (147) in the application database (50). When
data storage is complete, processing advances to a software block
402.
Analysis
[0110] The flow diagrams in FIG. 6A, FIG. 6B, FIG. 6C and FIG. 6D
detail the processing that is completed by the portion of the
application software (400) that continually generates the market
value matrix (see FIG. 10) by creating and activating analysis bots
that: [0111] 1. Identify the factor variables, factor performance
indicators and composite factors for each external factor that
drive: three of the segments of value--current operation,
derivatives and investments--as well as the components of current
operation value (revenue, expense and changes in capital); [0112]
2. Identify the item variables, item performance indicators and
composite variables for each element and sub-element of value that
drive: three segments of value--current operation, derivatives and
financial assets--as well as the components of current operation
value (revenue, expense and changes in capital); [0113] 3. Create
vectors that summarize the impact of the factor variables, factor
performance indicators and composite factors for each external
factor; [0114] 4. Create vectors that summarize the performance of
the item variables, item performance indicators and composite
variables for each element of value and sub-element of value in
driving segment value; [0115] 5. Determine the expected life of
each element of value and sub-element of value; [0116] 6. Determine
the current operation value, real option value, investment value
and derivative value, as well as revenue component value, expense
component value and capital component value of said current
operations using the information prepared in the previous stages of
processing; [0117] 7. Specify and optimize causal predictive models
to determine the relationship between the vectors generated in
steps 3 and 4 and three of the segments of value, current
operation, derivatives and investments, as well as the three
components of current operation value (revenue, expense and changes
in capital); [0118] 8. Identify likely scenarios for the evolution
of value drivers and event risks; [0119] 9. Quantify all risks
under a variety of scenarios for each enterprise; [0120] 10.
Determine the best causal indicator for enterprise stock price
movement, calculate market sentiment under the most likely scenario
and analyze the causes of market sentiment; and [0121] 11. Combine
the results of all prior stages of processing to determine the
value of each cell and cell subcategory within the market value
matrix. Each analysis bot generally normalizes the data being
analyzed before processing begins. As discussed previously,
processing in one embodiment includes an analysis of all five
segments of value for the organization, it is to be understood that
the system of the present invention can complete calculations for
any combination of the five segments. For example, when a company
is privately held it does not have a market price and as a result
the market sentiment segment of value is not analyzed.
[0122] Processing in this portion of the application begins in
software block 402. The software in block 402 checks the system
settings table (140) in the application database (50) to determine
if the current calculation is a new calculation or a structure
change. If the calculation is not a new calculation or a structure
change, then processing advances to a software block 418.
Alternatively, if the calculation is new or a structure change,
then processing advances to a software block 403.
[0123] The software in block 403 retrieves data from the system
settings table (140) and the matrix data table (143) and then
assigns unassigned item variables, item performance indicators and
composite variables to each element of value identified in the
system settings table (140) using a three-step process. First,
unassigned item variables, item performance indicators and
composite variables are assigned to elements of value based on the
asset management system they correspond to (for example, all item
variables from a brand management system and all item performance
indicators and composite variables derived from brand management
system item variables are assigned to the brand element of value).
Second, pre-defined composite variables are assigned to the element
of value they were assigned to measure in the system settings table
(140). Finally, item variables, item performance indicators and
composite variables identified by the text and geospatial bots are
assigned to elements on the basis of their element classifications.
If any item variables, item performance indicators or composite
variables are un-assigned at this point they are assigned to a
going concern element of value. After the assignment of variables
and indicators to elements is complete, the resulting assignments
are saved to the matrix data table (143) by enterprise and
processing advances to a block 404.
[0124] The software in block 404 retrieves data from the system
settings table (140), the matrix data table (143) and the frame
definition table (146) and then assigns unassigned factor
variables, factor performance indicators and composite factors to
each external factor. Factor variables, factor performance
indicators and composite factors identified by the text bots are
then assigned to factors on the basis of their factor
classifications. The resulting assignments are saved to the matrix
data table (143) by enterprise and processing advances to a block
405.
[0125] The software in block 405 checks the system settings table
(140) in the application database (50) to determine if any of the
enterprises in the organization being analyzed have market
sentiment segments. If there are market sentiment segments for any
enterprise, then processing advances to a block 406. Alternatively,
if there are no market prices for equity for any enterprise, then
processing advances to a software block 408.
[0126] The software in block 406 checks the bot date table (149)
and deactivates market value indicator bots with creation dates
before the current system date. The software in block 406 then
initializes market value indicator bots in accordance with the
frequency specified by the user (20) in the system settings table
(140). The bot retrieves the information from the system settings
table (140) and the matrix data table (143) before saving the
resulting information in the application database (50).
[0127] Bots are independent components of the application that have
specific tasks to perform. In the case of market value indicator
bots their primary task is to identify the best market value
indicator (price, relative price, yield, option price, first
derivative of price change or second derivative of price change)
for the time period being examined. The market value indicator bots
select the best value indicator by grouping the S&P 500 using
each of the five value indicators with a Kohonen neural network.
The resulting clusters are then compared to the known groupings of
the S&P 500. The market value indicator that produced the
clusters that most closely match the S&P 500 groupings is
selected as the market value indicator. Every market value
indicator bot contains the information shown in Table 23.
TABLE-US-00022 TABLE 23 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise
When bots in block 406 have identified and stored the best market
value indicator in the matrix data table (143), processing advances
to a block 407.
[0128] The software in block 407 checks the bot date table (149)
and deactivates temporal clustering bots with creation dates before
the current system date. The software in block 407 then initializes
a bot in accordance with the frequency specified by the user (20)
in the system settings table (140). The bot retrieves information
from the system settings table (140) and the matrix data table
(143) in order and define regimes for the enterprise market value
before saving the resulting cluster information in the application
database (50).
[0129] Bots are independent components of the application that have
specific tasks to perform. In the case of temporal clustering bots,
their primary task is to segment the market price data by
enterprise using the market value indicator selected by the bot in
block 406 into distinct time regimes that share similar
characteristics. The temporal clustering bot assigns a unique
identification (id) number to each "regime" it identifies before
tagging and storing the unique id numbers in the matrix data table
(143). Every time period with data are assigned to one of the
regimes. The cluster id for each regime is saved in the data record
for each piece of element data and factor data in the matrix data
table (143) by enterprise. If there are enterprises in the
organization that don't have market sentiment calculations, then
the time regimes from the primary enterprise specified by the user
in the system settings table (140) are used in labeling the data
for the other enterprises. The time periods are segmented for each
enterprise with a market value using a competitive regression
algorithm that identifies an overall, global model before splitting
the data and creating new models for the data in each partition. If
the error from the two models is greater than the error from the
global model, then there is only one regime in the data.
Alternatively, if the two models produce lower error than the
global model, then a third model is created. If the error from
three models is lower than from two models then a fourth model is
added. The process continues until adding a new model does not
improve accuracy. Other temporal clustering algorithms may be used
to the same effect. Every temporal clustering bot contains the
information shown in Table 24.
TABLE-US-00023 TABLE 24 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Maximum
number of clusters 6. Organization 7. Enterprise
When bots in block 407 have identified and stored regime
assignments for all time periods with data by enterprise,
processing advances to a software block 408.
[0130] The software in block 408 checks the bot date table (149)
and deactivates variable clustering bots with creation dates before
the current system date. The software in block 408 then initializes
bots in order for each element of value and external factor by
enterprise. The bots: activate in accordance with the frequency
specified by the user (20) in the system settings table (140),
retrieve the information from the system settings table (140) and
the matrix data table (143) and define segments for the element
data and factor data before tagging and saving the resulting
cluster information in the matrix data table (143).
[0131] Bots are independent components of the application that have
specific tasks to perform. In the case of variable clustering bots,
their primary task is to segment the element data and factor data
into distinct clusters that share similar characteristics. The
clustering bot assigns a unique id number to each "cluster" it
identifies, tags and stores the unique id numbers in the matrix
data table (143). Every item variable for every element of value is
assigned to one of the unique clusters. The cluster id for each
variable is saved in the data record for each variable in the table
where it resides. In a similar fashion, every factor variable for
every external factor is assigned to a unique cluster. The cluster
id for each variable is tagged and saved in the data record for the
factor variable. The element data and factor data are segmented
into a number of clusters less than or equal to the maximum
specified by the user (20) in the system settings table (140). The
data are segmented using the "default" clustering algorithm the
user (20) specified in the system settings table (140). The system
of the present invention provides the user (20) with the choice of
several clustering algorithms including: an unsupervised "Kohonen"
neural network, neural network, decision tree, support vector
method, K-nearest neighbor, expectation maximization (EM) and the
segmental K-means algorithm. For algorithms that normally require
the number of clusters to be specified, the bot will iterate the
number of clusters until it finds the cleanest segmentation for the
data. Every variable clustering bot contains the information shown
in Table 25.
TABLE-US-00024 TABLE 25 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Element of
value, sub element of value or external factor 6. Clustering
algorithm type 7. Organization 8. Enterprise 9. Maximum number of
clusters 10. Variable 1 . . . to 10 + n. Variable n
When bots in block 408 have identified, tagged and stored cluster
assignments for the data associated with each element of value,
sub-element of value or external factor in the matrix data table
(143), processing advances to a software block 409.
[0132] The software in block 409 checks the bot date table (149)
and deactivates predictive model bots with creation dates before
the current system date. The software in block 409 then retrieves
the information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing predictive
model bots for each component of value.
[0133] Bots are independent components of the application that have
specific tasks to perform. In the case of predictive model bots,
their primary task is to determine the relationship between the
element and factor data and the derivative segment of value, the
investment segment of value and the current operation segment of
value by enterprise. The predictive model bots also determine the
relationship between the element data and factor data and the
components of current operation value and sub-components of current
operation value by enterprise. Predictive model bots are
initialized for each component of value, sub-component of value,
derivative segment and investment segment by enterprise. They are
also initialized for each cluster and regime of data in accordance
with the cluster and regime assignments specified by the bots in
blocks 407 and 408 by enterprise. A series of predictive model bots
is initialized at this stage because it is impossible to know in
advance which predictive model type will produce the "best"
predictive model for the data from each commercial enterprise. The
series for each model includes 12 predictive model bot types:
neural network; CART; GARCH, projection pursuit regression;
generalized additive model (GAM), redundant regression network;
rough-set analysis, boosted Naive Bayes Regression; MARS; linear
regression; support vector method and stepwise regression.
Additional predictive model types can be used to the same effect.
The software in block 409 generates this series of predictive model
bots for the enterprise as shown in Table 26.
TABLE-US-00025 TABLE 26 PREDICTIVE MODELS BY ENTERPRISE LEVEL
Enterprise: Variables* relationship to enterprise cash flow
(revenue - expense + capital change) Variables* relationship to
enterprise revenue component of value Variables* relationship to
enterprise expense subcomponents of value Variables* relationship
to enterprise capital change subcomponents of value Variables*
relationship to derivative segment of value Variables* relationship
to investment segment of value Element of Value: Sub-element of
value variables relationship to element of value *Variables =
element and factor data.
[0134] Every predictive model bot contains the information shown in
Table 27.
TABLE-US-00026 TABLE 27 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Global or Cluster (ID) and/or Regime (ID) 8.
Segment (derivative, investment or current operation) 9. Element,
sub-element or external factor 10. Predictive model type
[0135] After predictive model bots are initialized, the bots
activate in accordance with the frequency specified by the user
(20) in the system settings table (140). Once activated, the bots
retrieve the required data from the appropriate table in the
application database (50) and randomly partition the element or
factor data into a training set and a test set. The software in
block 409 uses "bootstrapping" where the different training data
sets are created by re-sampling with replacement from the original
training set so data records may occur more than once. After the
predictive model bots complete their training and testing,
processing advances to a block 410.
[0136] The software in block 410 determines if clustering improved
the accuracy of the predictive models generated by the bots in
software block 409 by enterprise. The software in block 410 uses a
variable selection algorithm such as stepwise regression (other
types of variable selection algorithms can be used) to combine the
results from the predictive model bot analyses for each type of
analysis--with and without clustering--to determine the best set of
variables for each type of analysis. The type of analysis having
the smallest amount of error as measured by applying the mean
squared error algorithm to the test data are given preference in
determining the best set of variables for use in later analysis.
There are four possible outcomes from this analysis as shown in
Table 28.
TABLE-US-00027 TABLE 28 1. Best model has no clustering 2. Best
model has temporal clustering, no variable clustering 3. Best model
has variable clustering, no temporal clustering 4. Best model has
temporal clustering and variable clustering
If the software in block 410 determines that clustering improves
the accuracy of the predictive models for an enterprise, then
processing advances to a software block 413. Alternatively, if
clustering does not improve the overall accuracy of the predictive
models for an enterprise, then processing advances to a software
block 411.
[0137] The software in block 411 uses a variable selection
algorithm such as stepwise regression (other types of variable
selection algorithms can be used) to combine the results from the
predictive model bot analyses for each model to determine the best
set of variables for each model. The models having the smallest
amount of error, as measured by applying the mean squared error
algorithm to the test data, are given preference in determining the
best set of variables. As a result of this processing, the best set
of variables contain the: item variables, factor variables, item
performance indicators, factor performance indications, composite
variables and composite factors (aka element data and factor data)
that correlate most strongly with changes in the three segments
being analyzed and the three components of value. The best set of
variables will hereinafter be referred to as the "value
drivers".
[0138] Eliminating low correlation factors from the initial
configuration of the vector creation algorithms increases the
efficiency of the next stage of system processing. Other error
algorithms alone or in combination may be substituted for the mean
squared error algorithm. After the best set of variables have been
selected, tagged and stored in the matrix data table (143) for all
models at all levels for each enterprise in the organization, the
software in block 411 tests the independence of the value drivers
at the enterprise, external factor, element and sub-element level
before processing advances to a block 412.
[0139] The software in block 412 checks the bot date table (149)
and deactivates causal predictive model bots with creation dates
before the current system date. The software in block 412 then
retrieves the information from the system settings table (140) and
the matrix data table (143) as part of the process of initializing
causal predictive model bots for each element of value, sub-element
of value and external factor in accordance with the frequency
specified by the user (20) in the system settings table (140).
[0140] Bots are independent components of the application that have
specific tasks to perform. In the case of causal predictive model
bots, their primary task is to refine the value driver selection to
reflect only causal variables. (Note: these variables are summed
together to value an element when they are interdependent). A
series of causal predictive model bots are initialized at this
stage because it is impossible to know in advance which causal
predictive model will produce the "best" vector for the best fit
variables from each model. The series for each model includes five
causal predictive model bot types: Tetrad, MML, LaGrange, Bayesian
and path analysis. The software in block 412 generates this series
of causal predictive model bots for each set of value drivers
stored in the matrix data table (143) in the previous stage in
processing. Every causal predictive model bot activated in this
block contains the information shown in Table 29.
TABLE-US-00028 TABLE 29 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Component or
subcomponent of value 6. Element, sub-element or external factor 7.
Variable set 8. Causal predictive model type 9. Organization 10.
Enterprise
[0141] After the causal predictive model bots are initialized by
the software in block 412, the bots activate in accordance with the
frequency specified by the user (20) in the system settings table
(140). Once activated, they retrieve the required information for
each model and sub-divide the variables into two sets, one for
training and one for testing. After the causal predictive model
bots complete their processing for each model, the software in
block 412 uses a model selection algorithm to identify the model
that best fits the data for each element of value, sub-element of
value and external factor being analyzed. For the system of the
present invention, a cross validation algorithm is used for model
selection. The software in block 412 tags and saves the best fit
causal factors in the vector table (153) by enterprise in the
application database (50) and processing advances to a block
418.
[0142] The software in block 418 tests the value drivers to see if
there is interaction between elements, between elements and
external factors or between external factors by enterprise. The
software in this block identifies interaction by evaluating a
chosen model based on stochastic-driven pairs of value-driver
subsets. If the accuracy of such a model is higher that the
accuracy of statistically combined models trained on attribute
subsets, then the attributes from subsets are considered to be
interacting and then they form an interacting set. If the software
in block 418 does not detect any value driver interaction or
missing variables for each enterprise, then system processing
advances to a block 423. Alternatively, if missing data or value
driver interactions across elements are detected by the software in
block 418 for one or more enterprise, then processing advances to a
software block 421.
[0143] If software in block 410 determines that clustering improves
predictive model accuracy, then processing advances to block 413 as
described previously. The software in block 413 uses a variable
selection algorithm such as stepwise regression (other types of
variable selection algorithms can be used) to combine the results
from the predictive model bot analyses for each model, cluster
and/or regime to determine the best set of variables for each
model. The models having the smallest amount of error as measured
by applying the mean squared error algorithm to the test data are
given preference in determining the best set of variables. As a
result of this processing, the best set of variables contains: the
element data and factor data that correlate most strongly with
changes in the components of value. The best set of variables will
hereinafter be referred to as the "value drivers". Eliminating low
correlation factors from the initial configuration of the vector
creation algorithms increases the efficiency of the next stage of
system processing. Other error algorithms alone or in combination
may be substituted for the mean squared error algorithm. After the
best set of variables have been selected, tagged as value drivers
and stored in the matrix data table (143) for all models at all
levels by enterprise, the software in block 413 tests the
independence of the value drivers at the enterprise, element,
sub-element and external factor level before processing advances to
a block 414.
[0144] The software in block 414 checks the bot date table (149)
and deactivates causal predictive model bots with creation dates
before the current system date. The software in block 414 then
retrieves the information from the system settings table (140) and
the matrix data table (143) as part of the process of initializing
causal predictive model bots for each element of value, sub-element
of value and external factor at every level in accordance with the
frequency specified by the user (20) in the system settings table
(140).
[0145] Bots are independent components of the application that have
specific tasks to perform. In the case of causal predictive model
bots, their primary task is to refine the element and factor value
driver selection to reflect only causal variables. (Note: these
variables are grouped together to represent a single element vector
when they are dependent). In some cases it may be possible to skip
the correlation step before selecting causal the item variables,
factor variables, item performance indicators, factor performance
indicators, composite variables and composite factors (aka element
data and factor data). A series of causal predictive model bots are
initialized at this stage because it is impossible to know in
advance which causal predictive model will produce the "best"
vector for the best fit variables from each model. The series for
each model includes four causal predictive model bot types: Tetrad,
LaGrange, Bayesian and path analysis. The software in block 414
generates this series of causal predictive model bots for each set
of value drivers stored in the matrix data table (143) in the
previous stage in processing. Every causal predictive model bot
activated in this block contains the information shown in Table
30.
TABLE-US-00029 TABLE 30 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Component or
subcomponent of value 6. Cluster (ID) and/or Regime (ID) 7.
Element, sub-element or external factor 8. Variable set 9.
Organization 10. Enterprise 11. Causal predictive model type
[0146] After the causal predictive model bots are initialized by
the software in block 414, the bots activate in accordance with the
frequency specified by the user (20) in the system settings table
(140). Once activated, they retrieve the required information for
each model and sub-divide the variables into two sets, one for
training and one for testing. The same set of training data are
used by each of the different types of bots for each model. After
the causal predictive model bots complete their processing for each
model, the software in block 414 uses a model selection algorithm
to identify the model that best fits the data for each element,
sub-element or external factor being analyzed by model and/or
regime by enterprise. For the system of the present invention, a
cross validation algorithm is used for model selection. The
software in block 414 tags and saves the best fit causal factors in
the vector table (153) by enterprise in the application database
(50) and processing advances to block 418. The software in block
418 tests the value drivers to see if there are "missing" value
drivers that are influencing the results as well as testing to see
if there are interactions (dependencies) across elements and/or
external factors. If the software in block 418 does not detect any
missing data or value driver interactions across elements or
factors, then system processing advances to a block 423.
Alternatively, if missing data or value driver interactions across
elements or factors are detected by the software in block 418, then
processing advances to a software block 421.
[0147] The software in block 421 prompts the user (20) via the
structure revision window (710) to adjust the specification(s) for
the elements of value, sub-elements of value or external factors in
order to minimize or eliminate the interaction that was identified.
At this point the user (20) has the option of specifying that one
or more elements of value, sub elements of value and/or external
factors be combined for analysis purposes (element combinations
and/or factor combinations) for each enterprise where there is
interaction between elements and/or factors. The user (20) also has
the option of specifying that the elements or external factors that
are interacting will be valued by summing the impact of their
individual value drivers. Finally, the user (20) can choose to
re-assign a value driver to a new element of value or external
factor to eliminate the inter-dependency. This process is the
preferred solution when the inter-dependent value driver is
included in the going concern element of value. Elements and
external factors that will be valued by summing their value drivers
will not have vectors generated.
[0148] Elements of value and external factors do not share value
drivers and they are not combined with one another. However, when
an external factor and an element of value are shown to be
inter-dependent, it is usually because the element of value is a
dependent on the external factor. For example, the value of a
process typically varies with the price of commodities consumed in
the process. In that case, the value of both the external factor
and the element of value would be expected to be a function of the
same value driver. The software in block 421 examines all the
factor-element combinations and suggest the appropriate percentage
of factor risk assignment to the different elements it interacts
with. For example, 30% of a commodity factor risk could be
distributed to each of the 3 processes that consume the commodity
with the remaining 10% staying in the going concern element of
value. The user (20) either accepts the suggested distribution or
specifies his own distribution for each factor-element
interaction.
[0149] After the input from the user (20) is saved in the system
settings table (140) and the matrix data table (143) system
processing advances to a software block 423. The software in block
423 checks the system settings table (140) and the matrix data
table (143) to see if there any changes in structure. If there have
been changes in the structure, then processing advances to block
303 and the system processing described previously is repeated.
Alternatively, if there are no changes in structure, then
processing advances to a block 425.
[0150] The software in block 425 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new one. If the calculation is new, then
processing advances to a software block 426. Alternatively, if the
calculation is not a new calculation, then processing advances to a
software block 433.
[0151] The software in block 426 checks the bot date table (149)
and deactivates industry rank bots with creation dates before the
current system date. The software in block 426 then retrieves the
information from the system settings table (140) and the industry
ranking table (154) as part of the process of initializing industry
rank bots for the enterprise and for the industry in accordance
with the frequency specified by the user (20) in the system
settings table (140).
[0152] Bots are independent components of the application that have
specific tasks to perform. In the case of industry rank bots, their
primary task is to determine the relative position of each
enterprise being evaluated on element data identified in the
previous processing step. (Note: these variables are grouped
together when they are interdependent). The industry rank bots use
ranking algorithms such as Data Envelopment Analysis (hereinafter,
DEA) to determine the relative industry ranking of the enterprise
being examined. The software in block 426 generates industry rank
bots for each enterprise being evaluated. Every industry rank bot
activated in this block contains the information shown in Table
31.
TABLE-US-00030 TABLE 31 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Ranking
algorithm 6. Organization 7. Enterprise
[0153] After the industry rank bots are initialized by the software
in block 426, the bots activate in accordance with the frequency
specified by the user (20) in the system settings table (140). Once
activated, they retrieve the item variables, item performance
indicators, and composite variables from the application database
(50) and sub-divides them into two sets, one for training and one
for testing. After the industry rank bots develop and test their
rankings, the software in block 426 saves the industry rankings in
the industry ranking table (154) by enterprise in the application
database (50) and processing advances to a block 427. The industry
rankings are item variables.
[0154] The software in block 427 checks the bot date table (149)
and deactivates vector generation bots with creation dates before
the current system date. The software in block 427 then initializes
bots for each element of value, sub-element of value, element
combination, factor combination and external factor for each
enterprise in the organization. The bots activate in accordance
with the frequency specified by the user (20) in the system
settings table (140), retrieve the information from the system
settings table (140) and the matrix data table (143) as part of the
process of initializing vector generation bots for each element of
value and sub-element of value in accordance with the frequency
specified by the user (20) in the system settings table (140). Bots
are independent components of the application that have specific
tasks to perform. In the case of vector generation bots, their
primary task is to produce formulas, (hereinafter, vectors) that
summarize the relationship between the causal value drivers and
changes in the component or sub-component of value being examined
for each enterprise. The causal value drivers may be grouped by
element of value, sub-element of value, external factor, factor
combination or element combination. As discussed previously, the
vector generation step is skipped for value drivers where the user
has specified that value driver impacts will be mathematically
summed to determine the value of the element or factor. The vector
generation bots use induction algorithms to generate the vectors.
Other vector generation algorithms can be used to the same effect.
The software in block 427 generates a vector generation bot for
each set of causal value drivers stored in the matrix data table
(143). Every vector generation bot contains the information shown
in Table 32.
TABLE-US-00031 TABLE 32 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Element, sub-element, element combination, factor
or factor combination 8. Segment, component or sub-component of
value 9. Factor 1 . . . to 9 + n. Factor n
[0155] When bots in block 427 have identified, tagged and stored
vectors for all time periods with data for all the elements,
sub-elements, element combinations, factor combinations or external
factors where vectors are being calculated in the matrix data table
(143) and the vector table (153) by enterprise, processing advances
to a software block 429.
[0156] The software in block 429 checks the bot date table (149)
and deactivates financial factor bots with creation dates before
the current system date. The software in block 429 then retrieves
the information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing financial
factor bots for the enterprise and the relevant industry in
accordance with the frequency specified by the user (20) in the
system settings table (140).
[0157] Bots are independent components of the application that have
specific tasks to perform. In the case of financial factor bots,
their primary task is to identify elements of value, external
factors and value drivers that are causal factors for changes in
the value of: derivatives, investments, enterprise equity and
industry equity. The causal factors for enterprise equity and
industry equity are those that drive changes in the value indicator
identified by the value indicator bots. The series for each model
includes two causal predictive model bot types: Tetrad and path
analysis. Other causal predictive models can be used to the same
effect. The software in block 429 generates this series of causal
predictive model bots for each set of causal value drivers stored
in the matrix data table (143) in the previous stage in processing
by enterprise. Every financial factor bot activated in this block
contains the information shown in Table 33.
TABLE-US-00032 TABLE 33 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Element,
value driver or external factor 6. Organization 7. Enterprise 8.
Type: derivatives, investment, organization, enterprise or industry
equity 9. Value indicator (price, relative price, first derivative,
etc.) 10. Causal predictive model type
[0158] After the software in block 429 initializes the financial
factor bots, the bots activate in accordance with the frequency
specified by the user (20) in the system settings table (140). Once
activated, they retrieve the required information and sub-divide
the data into two sets, one for training and one for testing. The
same set of training data are used by each of the different types
of bots for each model. After the financial factor bots complete
their processing for each segment of value, enterprise and
industry, the software in block 429 uses a model selection
algorithm to identify the model that best fits the data for each.
For the system of the present invention, a cross validation
algorithm is used for model selection. The software in block 429
tags and saves the best fit causal value drivers in the matrix data
table (143) by enterprise and processing advances to a block 430.
The software in block 430 tests to see if there are "missing"
causal factors, elements or value drivers that are influencing the
results by enterprise. If the software in block 430 does not detect
any missing factors, elements or value drivers, then system
processing advances to a block 431. Alternatively, if missing
factors, elements or value drivers are detected by the software in
block 430, then processing returns to software block 421 and the
processing described in the preceding section is repeated.
[0159] The software in block 431 checks the bot date table (149)
and deactivates option bots with creation dates before the current
system date. The software in block 431 then retrieves the
information from the system settings table (140), the matrix data
table (143), the vector table (153) and the industry ranking table
(154) as part of the process of initializing option bots for the
enterprise.
[0160] Bots are independent components of the application that have
specific tasks to perform. In the case of option bots, their
primary tasks are to value the base value of the real options and
contingent liabilities for the enterprise. If the user (20) has
chosen to include industry options, then option bots will be
initialized for industry options as well. The discount rate for
enterprise real options, contingent liabilities and industry
options is calculated using a total cost of capital approach that
includes the cost of risk capital in a manner that is well known.
After the appropriate discount rate is determined, the value of
each real option and contingent liability is calculated using the
specified algorithms in a manner that is well known. The real
option can be valued using a number of algorithms including Black
Scholes, binomial, neural network or dynamic programming
algorithms. Every option bot contains the information shown in
Table 34.
TABLE-US-00033 TABLE 34 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Industry or Enterprise 7. Real option type (industry or
enterprise) 8. Real option algorithm (Black Scholes, quadranomial,
dynamic program, etc.)
After the option bots are initialized, they activate in accordance
with the frequency specified by the user (20) in the system
settings table (140). After being activated, the bots retrieve
information in order to complete the option valuations. When they
are used, industry option bots go on to allocate a percentage of
the calculated value of industry options to the enterprise on the
basis of causal element strength. After the value of the real
option, contingent liability or allocated industry option is
calculated the resulting values are tagged then saved in the matrix
data table (143) in the application database (50) by enterprise
before processing advances to a block 432. Alternative methods of
achieving the same results using the information in the matrix data
table (143) and the industry ranking table (154) would include
calculating an discount rate for each calculation that was a
function of the relative strength of the different elements of
value of each enterprise.
[0161] The software in block 432 checks the bot date table (149)
and deactivates cash flow bots with creation dates before the
current system date. The software in the block then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing cash flow
bots for each enterprise in accordance with the frequency specified
by the user (20) in the system settings table (140).
[0162] Bots are independent components of the application that have
specific tasks to perform. In the case of cash flow bots, their
primary tasks are to calculate the cash flow for each enterprise
for every time period where data are available and to forecast a
steady state cash flow for each enterprise in the organization.
Cash flow is calculated using the forecast revenue, expense,
capital change and depreciation data retrieved from the matrix data
table (143) with a well-known formula where cash flow equals period
revenue minus period expense plus the period change in capital plus
non-cash depreciation/amortization for the period. The steady state
cash flow for each enterprise is calculated for the enterprise
using forecasting methods identical to those disclosed previously
in U.S. Pat. No. 5,615,109 to forecast revenue, expenses, capital
changes and depreciation separately before calculating the cash
flow. Every cash flow bot contains the information shown in Table
35.
TABLE-US-00034 TABLE 35 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise
[0163] After the cash flow bots are initialized, the bots activate
in accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated the bots,
retrieve the forecast data for each enterprise from the matrix data
table (143) and then calculate a steady state cash flow forecast by
enterprise. The resulting values by period for each enterprise are
then stored in the cash flow table (141) in the application
database (50) before processing advances to a block 433.
[0164] The software in block 433 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 445. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 441.
[0165] The software in block 441 uses the cash flow by period data
from the cash flow table (141) and the calculated requirement for
working capital to calculate the value of excess cash and
marketable securities for every time period by enterprise and
stores the results of the calculation in the financial forecasts
table (150) in the application database. The excess cash and
marketable securities calculated in this step is added to the
forecast investment balance by period by enterprise and stored in
the financial forecasts table (150) before processing advances to a
block 442.
[0166] The software in block 442 checks the bot date table (149)
and deactivates financial value bots with creation dates before the
current system date. The software in block 442 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing financial
value bots for the derivatives and investments in accordance with
the frequency specified by the user (20) in the system settings
table (140).
[0167] Bots are independent components of the application that have
specific tasks to perform. In the case of financial value bots,
their primary task is to calculate the contribution of every
element of value, sub-element of value, element combination, value
driver, external factor and factor combination to the derivative
and investment segments of value by enterprise. The system of the
present invention uses 12 different types of predictive models to
determine relative contribution: neural network; CART; projection
pursuit regression; generalized additive model (GAM); GARCH; MMDR;
redundant regression network; boosted Naive Bayes Regression; the
support vector method; MARS; linear regression; and stepwise
regression. The model having the smallest amount of error as
measured by applying the mean squared error algorithm to the test
data are the best fit model. The "relative contribution algorithm"
used for completing the analysis varies with the model that was
selected as the "best-fit" as described previously. Every financial
value bot activated in this block contains the information shown in
Table 36.
TABLE-US-00035 TABLE 36 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Derivative or Investment 8. Element, sub-element,
factor, element combination, factor combination or value driver 9.
Predictive model type
After the software in block 442 initializes the financial value
bots, the bots activate in accordance with the frequency specified
by the user (20) in the system settings table (140). Once
activated, they retrieve the required information and sub-divide
the data into two sets, one for training and one for testing. The
same set of training data are used by each of the different types
of bots for each model. After the financial bots complete their
processing, the software in block 442 saves the calculated value
contributions in the matrix data table (143) by enterprise. The
calculated value contributions by element or external factor for
investments are also saved in the financial forecasts table (150)
by enterprise in the application database (50) and processing
advances to a block 443.
[0168] The software in block 443 checks the bot date table (149)
and deactivates element life bots with creation dates before the
current system date. The software in block 443 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing element
life bots for each element and sub-element of value for each
enterprise in the organization being analyzed.
[0169] Bots are independent components of the application that have
specific tasks to perform. In the case of element life bots, their
primary task is to determine the expected life of each element and
sub-element of value. There are three methods for evaluating the
expected life of the elements and sub-elements of value: [0170] 1.
Elements of value that are defined by a population of members or
items (such as: channel partners, customers, employees and vendors)
will have their lives estimated by analyzing and forecasting the
lives of the members of the population. The forecasting of member
lives will be determined by the "best" fit solution from competing
life estimation methods including the Iowa type survivor curves,
Weibull distribution survivor curves, Gompertz-Makeham survivor
curves, polynomial equations using the methodology for selecting
from competing forecasts disclosed in U.S. Pat. No. 5,615,109;
[0171] 2. Elements of value (such as patents, long term supply
agreements and insurance contracts) that have legally defined lives
will have their lives calculated using the time period between the
current date and the expiration date of the element or sub-element;
and [0172] 3. Finally, elements of value and sub-elements of value
(such as brand names, information technology and processes) that do
not have defined lives and/or that may not consist of a collection
of members will have their lives estimated as a function of the
enterprise Competitive Advantage Period (CAP).
[0173] In the latter case, the estimate will be completed using the
element vector trends and the stability of relative element
strength. More specifically, lives for these element types are
estimated by: subtracting time from the CAP for element volatility
that exceeds enterprise volatility and/or subtracting time for
relative element strength that is below the leading position and/or
relative element strength that is declining.
The resulting values are tagged and stored in the matrix data table
(143) for each element and sub-element of value by enterprise.
Every element life bot contains the information shown in Table
37.
TABLE-US-00036 TABLE 37 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Element or sub-element of value 8. Life estimation
method (item analysis, date calculation or relative to CAP)
After the element life bots are initialized, they are activated in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated, the bots
retrieve information for each element and sub-element of value from
the matrix data table (143) in order to complete the estimate of
element life. The resulting values are then tagged and stored in
the matrix data table (143) by enterprise in the application
database (50) before processing advances to a block 445.
[0174] The software in block 445 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 502. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 448.
[0175] The software in block 448 checks the bot date table (149)
and deactivates component capitalization bots with creation dates
before the current system date. The software in block 448 then
retrieves the information from the system settings table (140) and
the matrix data table (143) as part of the process of initializing
component capitalization bots for each enterprise in the
organization.
[0176] Bots are independent components of the application that have
specific tasks to perform. In the case of component capitalization
bots, their task is to determine the capitalized value of the
components and subcomponents of value--forecast revenue, forecast
expense or forecast changes in capital for each enterprise in the
organization in accordance with the formula shown in Table 38.
TABLE-US-00037 TABLE 38 Value = F.sub.f1/(1 + K) + F.sub.f2/(1 +
K).sup.2 + F.sub.f3/(1 + K).sup.3 + F.sub.f4/(1 + K).sup.4 +
(F.sub.f4 .times. (1 + g))/(1 + K).sup.5) + (F.sub.f4 .times. (1 +
g).sup.2)/(1 + K).sup.6) . . . + (F.sub.f4 .times. (1 +
g).sup.N)/(1 + K).sup.N+4) Where: F.sub.fx = Forecast revenue,
expense or capital requirements for year x after valuation date
(from advanced finance system) N = Number of years in CAP (from
prior calculation) K = Total average cost of capital - % per year
(from prior calculation) g = Forecast growth rate during CAP - %
per year (from advanced financial system)
After the calculation of capitalized value of every component and
sub-component of value is complete, the results are tagged and
stored in the matrix data table (143) by enterprise in the
application database (50). Every component capitalization bot
contains the information shown in Table 39.
TABLE-US-00038 TABLE 39 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Component of value (revenue, expense or capital
change) 8. Sub component of value
After the component capitalization bots are initialized, they
activate in accordance with the frequency specified by the user
(20) in the system settings table (140). After being activated, the
bots retrieve information for each component and sub-component of
value from the matrix data table (143) in order to calculate the
capitalized value of each component for each enterprise in the
organization. The resulting values are then tagged and saved in the
matrix data table (143) in the application database (50) by
enterprise before processing advances to a block 449.
[0177] The software in block 449 checks the bot date table (149)
and deactivates current operation bots with creation dates before
the current system date. The software in block 449 then retrieves
the information from the system settings table (140), the matrix
data table (143) and the financial forecasts table (150) as part of
the process of initializing bots for each element of value,
sub-element of value, combination of elements, value driver and/or
external factor for the current operation.
[0178] Bots are independent components of the application that have
specific tasks to perform. In the case of current operation bots,
their task is to calculate the contribution of every element of
value, sub-element of value, element combination, external factor,
factor combination and value driver to the current operation
segment of value. For calculating the current operation portion of
element value, the bots use the procedure outlined in Table 9. The
first step in completing the calculation in accordance with the
procedure outlined in Table 9, is determining the relative
contribution of each element, sub-element, combination of elements
or value driver by using a series of predictive models to find the
best fit relationship between: [0179] 1. The element of value
vectors, element combination vectors and external factor vectors,
factor combination vectors and value drivers and the enterprise
components of value they correspond to; and [0180] 2. The
sub-element of value vectors and the element of value they
correspond to.
[0181] The system of the present invention uses 12 different types
of predictive models to identify the best fit relationship: neural
network; CART; projection pursuit regression; generalized additive
model (GAM); GARCH; MMDR; redundant regression network; boosted
Naive Bayes Regression; the support vector method; MARS; linear
regression; and stepwise regression. The model having the smallest
amount of error as measured by applying the mean squared error
algorithm to the test data are the best fit model. The "relative
contribution algorithm" used for completing the analysis varies
with the model that was selected as the "best-fit". For example, if
the "best-fit" model is a neural net model, then the portion of
revenue attributable to each input vector is determined by the
formula shown in Table 40.
TABLE-US-00039 TABLE 40 ( k = 1 k = m j = 1 j = n l jk .times. O k
j = 1 j = n / k = 1 k = m l ik j = 1 j = n ) / l jk .times. O k
##EQU00001## Where ##EQU00001.2## l jk = Absolute value of the
input weight from input node j to hidden node k ##EQU00001.3## O k
= Absolute value of output weight from hidden node k M = number of
hidden nodes N = number of input nodes ##EQU00001.4##
[0182] After the relative contribution of each element of value,
sub-element of value, external factor, element combination, factor
combination and value driver to the components of current operation
value is determined, the results of this analysis are combined with
the previously calculated information regarding element life and
capitalized component value to complete the valuation of the
current operation contribution of each: element of value,
sub-element of value, external factor, element combination, factor
combination and/or value driver using the approach shown in Table
9.
[0183] The resulting values are tagged and stored in the matrix
data table (143) for each element of value, sub-element of value,
element combination, factor combination and value driver by
enterprise. For external factor and factor combination value
calculations, the external factor percentage is multiplied by the
capitalized component value to determine the external factor value.
The resulting values for external factors are also tagged and saved
in the matrix data table (143) by enterprise. Every current
operation bot contains the information shown in Table 41.
TABLE-US-00040 TABLE 41 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Element, sub-element, factor, element combination,
factor combination or value driver 8. Component of value (revenue,
expense or capital change)
After the current operation bots are initialized by the software in
block 449 they activate in accordance with the frequency specified
by the user (20) in the system settings table (140). After being
activated, the bots retrieve information and complete the valuation
for the component of value being analyzed. As described previously,
the resulting values are then tagged and saved in the matrix data
table (143) in the application database (50) by enterprise before
processing advances to a block 451.
[0184] The software in block 451 checks the bot date table (149)
and deactivates event risk bots with creation dates before the
current system date. The software in the block then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing event risk
bots for each enterprise in accordance with the frequency specified
by the user (20) in the system settings table (140). Bots are
independent components of the application that have specific tasks
to perform. In the case of event risk bots, their primary task is
to forecast the frequency and severity of event risks by
enterprise. In addition to forecasting insured risks, the system of
the present invention also uses historical data to forecast
"non-insured" standard risk such as the risk of employees resigning
and the risk of customers defecting. The system of the present
invention uses the forecasting methods disclosed in related U.S.
Pat. No. 5,615,109 for standard event risk forecasting and the game
theoretic real options models discussed in related U.S. patent Ser.
No. 10/036,522 for strategic risk forecasting. Every event risk bot
contains the information shown in Table 42.
TABLE-US-00041 TABLE 42 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Type: standard or strategic 8. Forecast method:
multivalent combination of forecasts or game theoretic real
option
After the event risk bots are initialized, the bots activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated the bots,
retrieve the data from the matrix data table (143) and then
forecast the frequency and severity of the event risks. The
resulting forecasts for each enterprise are then stored in the
matrix data table (143) before processing advances to a software
block 452 where statistics are calculated.
[0185] The software in block 452 checks the bot date table (149)
and deactivates statistical bots with creation dates before the
current system date. The software in block 452 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing statistical
bots for each causal value driver and event risk. Bots are
independent components of the application that have specific tasks
to perform. In the case of statistical bots, their primary tasks
are to calculate and store statistics such as mean, median,
standard deviation, slope, average period change, maximum period
change, variance and covariance between each causal value driver,
standard event risk and the S&P 500. Every statistical bot
contains the information shown in Table 43.
TABLE-US-00042 TABLE 43 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Value driver or standard event risk
When bots in block 452 have calculated, tagged and stored
statistics for each causal value driver and event risk in the
matrix data table (143) by enterprise, processing advances to a
software block 453.
[0186] The software in block 453 checks the bot date table (149)
and deactivates extreme value bots with creation dates before the
current system date. The software in block 453 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing extreme
value bots in accordance with the frequency specified by the user
(20) in the system settings table (140).
[0187] Bots are independent components of the application that have
specific tasks to perform. In the case of extreme value bots, their
primary task is to identify the extreme values for each causal
value driver. The extreme value bots use the Blocks method and the
peak over threshold method to identify extreme values. Other
extreme value algorithms can be used to the same effect. Every
extreme value bot activated in this block contains the information
shown in Table 44.
TABLE-US-00043 TABLE 44 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Method: blocks or peak over threshold 8. Value
driver or external factor
After the extreme value bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). Once activated, they retrieve the
required information and determine the extreme value range for each
value driver or external factor. The bot tags and saves the extreme
values for each causal value driver and external factor in the
matrix data table (143) by enterprise in the application database
(50) and processing advances to a block 454.
[0188] The software in block 454 checks the bot date table (149)
and deactivates forecast update bots with creation dates before the
current system date. The software in block 453 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing forecast
bots in accordance with the frequency specified by the user (20) in
the system settings table (140).
[0189] Bots are independent components of the application that have
specific tasks to perform. In the case of forecast update bots,
their task is to compare the forecasts stored for external factors
and financial asset values (subset of investments) with the
information available from futures exchanges. Every forecast update
bot activated in this block contains the information shown in Table
45.
TABLE-US-00044 TABLE 45 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. External factor or financial asset 8. Forecast
time period
After the forecast update bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). Once activated, they retrieve the
required information and determine if any forecasts need to be
changed to bring them in line with the market data on future
values. The bots save the updated forecasts in the appropriate
tables in the application database (50) by enterprise and
processing advances to a block 455.
[0190] The software in block 455 checks the bot date table (149)
and deactivates scenario bots with creation dates before the
current system date. The software in block 455 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing scenario
bots in accordance with the frequency specified by the user (20) in
the system settings table (140).
[0191] Bots are independent components of the application that have
specific tasks to perform. In the case of scenario bots, their
primary task is to identify likely scenarios for the evolution of
the causal value drivers and event risks by enterprise. The
scenario bots use information from the advanced finance system,
external databases and the forecasts completed in the prior stage
to obtain forecasts for specific value drivers and event risks
before using the covariance information stored in the matrix data
table (143) to develop forecasts for the other causal value drivers
and risks under normal conditions. They also use the extreme value
information calculated by the previous bots and stored in the
matrix data table (143) to calculate extreme scenarios. Every
scenario bot activated in this block contains the information shown
in Table 46.
TABLE-US-00045 TABLE 46 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type: normal,
extreme 6. Organization 7. Enterprise
After the scenario bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). Once activated, they retrieve the
required information and develop a variety of scenarios as
described previously. After the scenario bots complete their
calculations, they save the resulting scenarios in the scenarios
table (152) by enterprise in the application database (50) and
processing advances to a block 456. If a most likely scenario has
been specified by the user (20) in the system settings table (140),
then the values for that scenario are calculated using a weighted
sum of the normal and extreme scenarios based on the percentages
specified by the user (20). The resulting "most likely" scenario is
also saved in the scenarios table (152) in the application database
(50).
[0192] The software in block 456 checks the bot date table (149)
and deactivates simulation bots with creation dates before the
current system date. The software in block 456 then retrieves the
information from the system settings table (140), the matrix data
table (143) and the scenarios table (152) as part of the process of
initializing simulation bots in accordance with the frequency
specified by the user (20) in the system settings table (140).
[0193] Bots are independent components of the application that have
specific tasks to perform. In the case of simulation bots, their
primary task is to run three different types of simulations for the
enterprise. The simulation bots run probabilistic simulations of
organizational financial performance and valuation by segment of
value for each enterprise using: the normal scenario and the
extreme scenario if no most likely scenario has been specified. If
a most likely scenario has been specified, then that scenario is
used. They also run an unconstrained genetic algorithm simulation
that evolves to the most negative value possible over the specified
time period. In one embodiment, Monte Carlo models are used to
complete the probabilistic simulation, however other probabilistic
simulation models such as Quasi Monte Carlo can be used to the same
effect. The models are initialized using the statistics and
relationships derived from the calculations completed in the prior
stages of processing to relate segment value to the value driver
and standard event risk scenarios. Every simulation bot activated
in this block contains the information shown in Table 47.
TABLE-US-00046 TABLE 47 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type: normal,
extreme, most likely and/or unconstrained genetic algorithm 6.
Segment of value: current operation, investments or derivatives 7.
Organization 8. Enterprise
After the simulation bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). Once activated, they retrieve the
required information and simulate the financial performance of the
different segments of value of the organization by enterprise over
the time periods specified by the user (20) in the system settings
table (140). In doing so, the bots will forecast the range of risk
and return that can be expected from each segment of value by
enterprise within the confidence interval defined by the user (20)
in the system settings table (140) for each scenario. After the
simulation bots complete their calculations, the resulting
forecasts are saved in the matrix data table (143), the summary
data table (156) and the simulation table (157) by enterprise in
the application database (50) and processing advances to a block
457.
[0194] The software in block 457 checks the bot date table (149)
and deactivates options simulation bots with creation dates before
the current system date. The software in block 457 then retrieves
the information from the system settings table (140), the matrix
data table (143) and the scenarios table (152) as part of the
process of initializing option simulation bots in accordance with
the frequency specified by the user (20) in the system settings
table (140).
[0195] Bots are independent components of the application that have
specific tasks to perform. In the case of option simulation bots,
their primary task is to run up to four different types of
simulations for the enterprise real options, contingent liabilities
and strategic risks. The option simulation bots run a normal
scenario and an extreme scenario if a most likely scenario has not
been specified. If a most likely scenario has been specified, then
it is used in the option simulations. In either case an
unconstrained genetic algorithm simulation that evolves to the most
negative value possible over the specified time period is analyzed.
The bots also run sensitivity analyses to determine the effect of
each value driver and event risk on option valuation under each
scenario. In one embodiment, Monte Carlo models are used to
complete the probabilistic simulation, however other probabilistic
simulation models such as Quasi Monte Carlo can be used to the same
effect. The models are initialized specifications used in the
baseline calculations. Every option simulation bot activated in
this block contains the information shown in Table 48.
TABLE-US-00047 TABLE 48 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type: normal,
extreme, unconstrained genetic algorithm or sensitivity 6. Option
type: real option, contingent liability or strategic risk 7.
Organization 8. Enterprise
After the option simulation bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). Once activated, they retrieve the
required information and simulate the financial performance of the
different types of options over the time periods specified by the
user (20) in the system settings table (140). In doing so, the bots
will forecast the range of values that can be expected from each
option type by enterprise within the confidence interval defined by
the user (20) in the system settings table (140) for each scenario.
The data from the sensitivity bots help distribute the factor risk
and element risk by option. After the option simulation bots
complete their calculations, the resulting forecasts are saved in
the matrix data table (143), the summary data table (156) and the
simulation table (157) by enterprise in the application database
(50) and processing advances to a block 458.
[0196] The software in block 458 checks the bot date table (149)
and deactivates segmentation bots with creation dates before the
current system date. The software in the block then retrieves the
information from the system settings table (140), the matrix data
table (143) and the simulation table (157) as part of the process
of initializing segmentation bots for each enterprise in accordance
with the frequency specified by the user (20) in the system
settings table (140). Bots are independent components of the
application that have specific tasks to perform. In the case of
segment bots, their primary task is to use the historical data and
simulation data segment the value of each element, factor, element
combination, factor combination and value driver into a base value
and a variability or risk component. The system of the present
invention uses wavelet algorithms to segment the value into two
components although other segmentation algorithms such as GARCH
could be used to the same effect. Every segmentation bot contains
the information shown in Table 49.
TABLE-US-00048 TABLE 49 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Element, sub-element, factor, element combination,
factor combination or value driver 8. Segmentation algorithm
After the segmentation bots are initialized, the bots activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated the bots
retrieve the data from the matrix data table (143) and then segment
the value of each element, factor, element combination, factor
combination or value driver into two segments. The resulting values
by period for each enterprise are then stored in the matrix data
table (143). As part of this processing the factor risk assignments
stored by the user (20) after interacting with the software in
block 421 are used to distribute factor risks to the elements of
value before processing advances to a software block 465 where the
event risks are analyzed.
[0197] The software in block 465 checks the bot date table (149)
and deactivates market risk bots with creation dates before the
current system date. The software in the block then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing market risk
bots for the market portfolio and for each enterprise with a market
value in accordance with the frequency specified by the user (20)
in the system settings table (140). Bots are independent components
of the application that have specific tasks to perform. In the case
of market risk bots, their tasks are to determine the implied
market risk for the analysis time periods for the market portfolio
and for each equity of each enterprise with a public market value
and to determine the market price of a unit of risk. The market
price of risk is the excess return the market requires per unit of
volatility. This value can be calculated using the traditional
capital asset pricing model in a manner that is well known. The
implied risk of each equity is determined using the Black Scholes
option pricing algorithm. The Black Scholes algorithm determines
the price for an equity option as a function of several variables
including the volatility of the equity. When the market price and
the other variables in the equation are known, then the Black
Scholes algorithm can be used to calculate the implied volatility
in the equity. Under the traditional capital asset pricing model
volatility equals market risk. Three moment and game-theoretic
capital asset pricing models can also be used to calculate an
overall market risk measure to the same effect. Every market risk
bot contains the information shown in Table 51.
TABLE-US-00049 TABLE 51 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise or market portfolio 7. Time period(s) 8. Overall
market risk measure: implied option volatility
After the market risk bots are initialized, the bots activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated the bots,
retrieve the data for each enterprise with a market price from the
matrix data table (143) and then calculate the implied volatility
for each time period. They also calculate the market price of risk
implied by the current price levels. The market price of risk is
then combined with the implied volatilities to calculate the market
value of risk for each time period. The resulting values for each
time period are then stored in the matrix data table (143) for the
market portfolio and for each enterprise before processing advances
to a software block 469 where market volatility is calculated.
[0198] The software in block 469 checks the bot date table (149)
and deactivates market volatility bots with creation dates before
the current system date. The software in the block then retrieves
the information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing market
volatility bots for the organization in accordance frequency
specified by the user (20) in the system settings table (140). Bots
are independent components of the application that have specific
tasks to perform. In the case of market volatility bots they have
two primary tasks. The first task is to transform the previously
completed calculations regarding event risk, strategic event risk
element variability risk and factor variability risk into forms
where they can be added together. The transformation of the risks
is completed by first transforming the event risk information to
normal variables. The transformed risk is then combined with the
market price of risk information derived previously so that the
layers of the event risks can be more readily compared with the
element and factor variability data. The second task is to compare
the implied market risk calculated by the bots in block 465 with
the summed total of the event, contingent event, strategic event,
element, factor and base market (or industry market) risks for the
specified time periods. As discussed previously, market volatility
is defined as the difference between market risk and the total of
all other types of risk. If the organization does not have a market
value, then the bots only complete the first task so that the
overall total risk can be calculated. Every market volatility bot
contains the information shown in Table 52.
TABLE-US-00050 TABLE 52 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Time Period(s)
[0199] After the market volatility bots are initialized, the bots
activate in accordance with the frequency specified by the user
(20) in the system settings table (140). After being activated the
bots, retrieve the data for the organization from the matrix data
table (143) and then calculate the total of the event, element
variability and factor variability risks after the transformations
have been completed. If there is a market price, then the value of
the market volatility is also calculated. The resulting values for
each time period for each enterprise and the organization are then
stored in the matrix data table (143) in the application database
(50) before processing advances to a software block 470.
[0200] The software in block 470 calculates the values for a number
of the cells in the market value matrix. First, the software in
block 470 uses the results of the sensitivity analysis completed by
the option bots in block 457 to calculate the impact of each
element of value and external factor on the real option segment of
value and save the resulting values in the matrix data table (143).
It then uses the factor risk assignments developed by the software
in block 421 to assign factor risks to the appropriate elements of
value and save the results in the matrix data table (143). Finally,
the software in block 470 uses the formula shown in Table 53 to
calculate the Current Operation Going Concern Value.
TABLE-US-00051 TABLE 53 Current Operation Going Concern Value =
Total Current Operation Value - .SIGMA. Financial Asset Values -
.SIGMA. Elements of Value - .SIGMA. External Factors - .SIGMA.
Current Operation Risks
[0201] After the current operation going concern value is
calculated, the resulting value is saved in the matrix data table
(143) before processing advances to a software block 471.
[0202] The software in block 471 checks the bot date table (149)
and deactivates value sentiment bots with creation dates before the
current system date. The software in block 471 then retrieves the
information from the system settings table (140) and the matrix
data table (143) as part of the process of initializing sentiment
calculation bots for the organization. Bots are independent
components of the application that have specific tasks to perform.
In the case of sentiment calculation bots, their task is to
retrieve data and then calculate the value of sentiment for each
enterprise in accordance with the formula shown in Table 54.
TABLE-US-00052 TABLE 54 Value of Market Sentiment = Market Value
for Enterprise - Current Operation Value - .SIGMA.Real Option
Values - .SIGMA.Value of Investments - .SIGMA. Derivative Values -
Market Value of Risk
[0203] Organizations that are not public corporations will, of
course, not have a market value so no calculation will be completed
for these enterprises. The market sentiment for the organization
will be calculated by subtracting the total for each of the other
four segments of value and the market value of risk for all
enterprises in the organization from the total market value for all
enterprises in the organization. Every value sentiment bot contains
the information shown in Table 55.
TABLE-US-00053 TABLE 55 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Enterprise 7. Type: Organization or Enterprise
After the value sentiment bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated, the bots
retrieve information from the system settings table (140), the
matrix data table (143) and the financial forecasts table (150) in
order to complete the sentiment calculation for each enterprise and
the organization. After the calculation is complete, the resulting
values are tagged then saved in the matrix data table (143) in the
application database (50) before processing advances to a block
473.
[0204] The software in block 473 checks the bot date table (149)
and deactivates sentiment analysis bots with creation dates before
the current system date. The software in block 473 then retrieves
the information from the system settings table (140), the matrix
data table (143) and the vector table (153) as part of the process
of initializing sentiment analysis bots for the enterprise. Bots
are independent components of the application that have specific
tasks to perform. In the case of sentiment analysis bots, their
primary task is to determine the composition of the calculated
sentiment for each enterprise in the organization and the
organization as a whole. One part of this analysis is completed by
comparing the portion of overall market value that is driven by the
different elements of value as determined by the bots in software
block 429 and the calculated valuation impact of each element of
value on the segments of value as shown below in Table 56.
TABLE-US-00054 TABLE 56 Total Enterprise Market Value = $100
Billion, 10% driven by Brand factors Implied Brand Value = $100
Billion .times. 10% = $10 Billion Brand Element Current Operation
Value = $6 Billion Increase/(Decrease) in Enterprise Real Option
Values* Due to Brand = $1.5 Billion Increase/(Decrease) in
Derivative Values due to Brands = $0.0 Increase/(Decrease) in
investment Values due to Brands = $0.25 Billion Brand Sentiment =
$10 - $6 - $1.5 - $0.0 - $0.25 = $2.25 Billion *includes allocated
industry options when used in the calculation
[0205] The sentiment analysis bots also determine the impact of
external factors on sentiment. Every sentiment analysis bot
contains the information shown in Table 57.
TABLE-US-00055 TABLE 57 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. External
factor or element of value 6. Organization 7. Enterprise
After the sentiment analysis bots are initialized, they activate in
accordance with the frequency specified by the user (20) in the
system settings table (140). After being activated, the bots
retrieve information from the system settings table (140), the
matrix data table (143) and the financial forecasts table (150) in
order to analyze sentiment. The resulting breakdown of sentiment is
tagged then saved in the matrix data table (143) by enterprise in
the application database (50). Sentiment at the organization level
is calculated by adding together the sentiment calculations for all
the enterprises in the organization. The results of this
calculation are also tagged and saved in the matrix data table
(143) in the application database (50) before processing advances
to a block 475
[0206] Before going on to discuss organization optimization
calculations is appropriate to briefly review the processing that
has been completed so far. At this point, the organization risk
matrix (FIG. 9) and the market value matrix (FIG. 10) have been
filled in with values for the organization at the date of system
calculation (assumes complete set of data up to and including the
date of system calculation has been processed). As detailed above,
the matrix of risk includes six types of risk--the risk associated
with element variability, factor variability, standard events,
strategic events, the base market (or industry market) portfolio
and market volatility. To the extent possible, the factor
variability risk, event risk, strategic event risk standard event
risk and market volatility have been placed in the matrix cell that
corresponds to the element of value and segment of value that the
risk corresponds to. External factors that have value as well as
all other risks that have not been distributed to an element of
value are left in the "going concern" element of value. In addition
to giving organizations a new level of control over the management
of their operational and financial performance. The system of the
present invention also greatly enhances the ability to develop:
securities that bundle risks together for resale, securities that
mix risk transfer products with equity ownership, services that
transfer risk in an automated fashion and strong working
relationships with external partners.
[0207] The software in block 475 checks the system settings table
(140) in the application database (50) to determine if the current
calculation is a new calculation or a structure change. If the
calculation is not a new calculation or a structure change, then
processing advances to a software block 502. Alternatively, if the
calculation is new or a structure change, then processing advances
to a software block 476.
[0208] The software in block 476 checks the bot date table (149)
and deactivates optimization bots with creation dates before the
current system date. The software in block 476 then retrieves the
information from the system settings table (140), the matrix data
table (143), the scenarios table (152) and the simulation table
(157) required to initialize value optimization bots in accordance
with the frequency specified by the user (20) in the system
settings table (140).
[0209] Bots are independent components of the application that have
specific tasks to perform. In the case of optimization bots, their
primary task is to determine the optimal mix of features for the
organization under a variety of scenarios for the specified time
period (or time periods). The optimal mix of features is the mix
that maximizes the value of the market value matrix at the end of
the given time period. A mixed integer non linear optimization is
used to determine the best mix of features for each scenario and
time period combination. Other optimization algorithms such as
genetic algorithms can be used at this point to achieve the same
result. Every optimization bot contains the information shown in
Table 58.
TABLE-US-00056 TABLE 58 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Scenario: normal, extreme and most likely 7. Time period
After the software in block 476 initializes the optimization bots,
they activate in accordance with the frequency specified by the
user (20) in the system settings table (140). After completing
their calculations, the resulting feature mix for each scenario and
is saved in the summary data table (156) in the application
database (50) by enterprise. The shadow prices from these
optimizations are also stored in the feature rank table (158) by
enterprise for use in identifying new features and feature options
that the company may wish to develop and/or purchase. After the
results of this optimization are stored in the application database
(50) by enterprise, processing advances to a software block
475.
[0210] The software in block 478 checks the bot date table (149)
and deactivates feature rank bots with creation dates before the
current system date. The software in block 478 then retrieves the
information from the system settings table (140), the matrix data
table (143), the summary data table (156) and the feature rank
table (158) as part of the process of initializing feature rank
bots for every feature and causal value driver.
[0211] Bots are independent components of the application that have
specific tasks to perform. In the case of feature rank bots, their
primary task is to rank all of the features, feature options and
causal value drivers that the organization can change to improve
value and/or reduce risk. Causal value drivers are analyzed to give
the user (20) insight into actions that may improve value that
haven't been identified as features. The feature rank bots rely on
the market value matrix developed in the prior stage of processing
to rank all of the different features and feature options that are
available to the system for financial measurement, management and
optimization. Every feature, feature option and value driver is
ranked on the basis its value impact, risk impact and overall value
impact net of investment for each scenario. Features, options and
value drivers are also ranked on the basis of capital efficiency
which is their overall value impact before deducting capital
investment over the capital investment required to implement the
feature or feature option. Features, options and value drivers that
do not require capital investment will have their value impact
divided by 0.01 to determine their capital efficiency ranking.
Every feature rank bot contains the information shown in Table
59.
TABLE-US-00057 TABLE 59 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Scenario: normal, extreme and most likely 7. Feature, feature
option or causal value driver
After the software in block 478 initializes the feature rank bots,
they activate in accordance with the frequency specified by the
user (20) in the system settings table (140). After completing
their calculations, the bots store the ranking for every feature,
feature option and causal value driver in the feature rank table
(158) by enterprise before processing advances to a software block
481.
[0212] The software in block 481 checks the bot date table (149)
and deactivates frontier bots with creation dates before the
current system date. The software in block 481 then retrieves the
information from the system settings table (140), the matrix data
table (143), the summary data table (156) and the feature rank
table (158) as part of the process of initializing frontier bots
for each scenario.
[0213] Bots are independent components of the application that have
specific tasks to perform. In the case of frontier bots, their
primary task is to define the efficient frontier for organization
financial performance under each scenario. The top leg of the
efficient frontier for each scenario is defined by successively
adding the features, options and value drivers that increase value
while increasing risk to the optimal mix in capital efficiency
order. The bottom leg of the efficient frontier for each scenario
is defined by successively adding the features, options and value
drivers that decrease value while decreasing risk to the optimal
mix in capital efficiency order. Every frontier bot contains the
information shown in Table 60.
TABLE-US-00058 TABLE 60 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Organization
6. Scenario: normal, extreme and most likely 7. Feature, feature
option or causal value driver
After the software in block 481 initializes the feature rank bots,
they activate in accordance with the frequency specified by the
user (20) in the system settings table (140). After completing
their calculations, the results of all 3 sets of calculations
(normal, extreme and most likely) are saved in the report table
(155) in sufficient detail to generate a chart like the one shown
in FIG. 11 before processing advances to a block 502.
Analysis & Output
[0214] The flow diagram in FIG. 7 details the processing that is
completed by the portion of the application software (500) that
generates the market value matrix for the organization, generates a
summary of the value, risk and liquidity for the organization,
analyzes changes in organization structure and operation and
optionally displays and prints management reports detailing the
value matrix, risk matrix and the efficient frontier. Processing in
this portion of the application starts in software block 502.
[0215] The software in block 502 retrieves information from the
system settings table (140), the cash flow table (141), the matrix
data table (143) and the financial forecasts table (150) that is
required to calculate the minimum amount of working capital that
will be available during the forecast time period. The system
settings table (140) contains the minimum amount of working capital
that the user (20) indicated was required for enterprise operation
while the cash flow table (141) contains a forecast of the cash
flow of the enterprise for each period during the forecast time
period (generally the next 36 months). A summary of the available
cash and cash deficits by currency, by month, for the next 36
months is stored in a summary xml format in the summary data table
(156) by enterprise during this stage of processing. After the
amount of available cash for each enterprise and the organization
is calculated and stored in the feature rank table (158),
processing advances to a software block 503.
[0216] The software in block 452 retrieves information from the
matrix data table (143), financial forecasts table (150) and the
summary data table (156) in order to generate the market value
matrix (FIG. 10) by enterprise for the organization for each
scenario. The matrices are stored in the report table (155) and a
summary version of the data are added to the summary data table
(156). The software in this block also creates and displays a
summary Market Value Map.TM. Report and a risk and return analysis
for the organization via the analysis definition window (708). The
software in the block then prompts the user (20) via the analysis
definition window (708) to specify changes in the organization that
should be analyzed. The user (20) is given the option of: adding
new features and feature options, re-defining the structure for
analysis purposes, examining the impact of changes in segments of
value, components of value, elements of value and/or external
factors on organization market value. For example, the user (20)
may wish to: [0217] 1. Redefine the efficient frontier without
considering the impact of market sentiment on organization
value--this analysis would be completed by temporarily re-defining
the structure and completing a new analysis; [0218] 2. Redefine the
efficient frontier after adding in the market value matrix for
another enterprise that may be purchased--this analysis would be
completed by temporarily re-defining the structure and completing a
new analysis; [0219] 3. Forecast the likely impact of a project on
organization value and risk--this analysis would be completed by
mapping the expected results of the project to the market value
matrix and then repeating the processing to determine if the
organization would be closer to or further from the original
efficient frontier after project implementation; [0220] 4. Forecast
the impact of changing economic conditions on the organizations
ability to repay its debt--this analysis would be completed by
mapping the expected changes to organization market value matrix,
recalculating value, liquidity and risk and then determining if the
organization will in a better position to repay its debt; or [0221]
5. Maximize revenue from all enterprises in the organization--this
analysis would be completed by temporarily defining a new structure
that included only the revenue component of value and repeating the
processing described previously. The software in block 503 saves
the analysis definitions the user (20) specifies in the analysis
definition table (148) in the application database (50) before
processing advances to a software block 506.
[0222] The software in block 506 checks the analysis definition
table (148) in the application database (50) to determine if the
user (20) has specified an analysis for computation. If an analysis
has been specified, then processing returns to block 303 and the
processing described previously is repeated with the changes
defined in the analysis definition table being used in completing
system calculations. After the analysis run is completed, the
software in block 508 displays the results of the analysis via the
analysis definition window (708) before processing advances to a
software block 510. Alternatively, if the user (20) did not request
an analysis, then processing advances directly to a software block
510.
[0223] The software in block 510 prompts the user (20) to
optionally select reports for display and/or printing using one or
more frames. The format of the reports is either graphical, numeric
or both depending on the type of report the user (20) specified in
the system settings table (140). Typical formats for graphical
reports displaying the efficient frontier are shown in FIG. 11 and
FIG. 12. The user can also choose to have reports displayed and/or
printed that compare the actual and forecast risk and return for
the organization to the risk and return for the benchmark return
previously saved in the benchmark return table (147). The report
can also show if the expected return from the organization differs
from the return that would be expected given the difference between
the risk of the organization and the risk of the market portfolio.
The expected difference in return can be calculated using the
different versions of the capital asset pricing model. If the user
(20) selects any reports for printing, then the information
regarding the selected reports is saved in the report table (155).
After the user (20) has finished selecting reports, the selected
reports are displayed to the user (20). After the user (20)
indicates that the review of the reports has been completed,
processing advances to a software block 511.
[0224] The software in block 511 prompts the user (20) to
optionally review the new market value matrix information by frame
and the responses to partner requests before they are released to
the software in block 210 for distribution. After the review is
complete processing passes to a software block 512. The processing
can also pass to block 512 if the maximum amount of time to wait
for no response specified by the user (20) in the system settings
table is exceeded and the user (20) has not responded.
[0225] The software in block 512 checks the report table (155) to
determine if any reports have been designated for printing. If
reports have been designated for printing, then processing advances
to a block 515. It should be noted that in addition to standard
reports like the market value matrix (FIG. 10), the matrix of
organization risk (FIG. 9), the Market Value Map.TM. report and the
graphical depictions of the efficient frontier shown in FIG. 11 and
FIG. 12, the system of the present invention can generate reports
that rank the elements, external factors and/or the risks in order
of their importance to market value and/or market risk by
enterprise, by segment of value and/or for the organization as a
whole. The system can also produce "metrics" reports by tracing the
historical measures for value drivers over time. The software in
block 515 sends the designated reports to the printer (118). After
the reports have been sent to the printer (118), processing
advances to a software block 517. Alternatively, if no reports were
designated for printing, then processing advances directly from
block 512 to block 517.
[0226] The software in block 517 checks the system settings table
(140) to determine if the system is operating in a continuous run
mode. If the system is operating in a continuous run mode, then
processing returns to block 303 and the processing described
previously is repeated in accordance with the frequency specified
by the user (20) in the system settings table (140). Alternatively,
if the system is not running in continuous mode, then the
processing advances to a block 518 where the system stops.
[0227] Thus, the reader will see that the system and method
described above transforms disparate narrow systems and knowledge
bases into an integrated system for measuring and optimizing the
financial performance of a multi-enterprise organization. The level
of detail, breadth and speed of the financial analysis gives users
of the integrated system the ability to manage their operations in
an fashion that is superior to any method currently available to
users of the isolated, narrowly focused management systems.
[0228] While the above description contains many specificities,
these should not be construed as limitations on the scope of the
invention, but rather as an exemplification of one preferred
embodiment thereof. Accordingly, the scope of the invention should
be determined not by the embodiment illustrated, but by the
appended claims and their legal equivalents.
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